Careers at Nebius Group

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius is looking for a Senior Data Engineer who in addition to building and owning data pipelines will also drive the design and technical leadership within the data engineering team. This is a hands-on data engineering role, focused on designing, implementing, and maintaining reliable data flows for analytics and machine learning. Infrastructure, cloud, and Kubernetes are used only as tools to run pipelines reliably and cost-efficiently — this is not an SRE or platform engineering role. You’re welcome to work in our offices in Tel Aviv, Israel. Your responsibilities will include: Core Responsibilities (Primary Focus) Design, build, and own production-grade data pipelines using Python and SQL. Develop stateless, idempotent pipelines that are resilient to retries, failures, and infrastructure interruptions. Implement data transformations, validation, and data quality checks. Optimize pipelines for performance, reliability, and cost efficiency. Collaborate closely with Analytics, Data Science, and ML teams to deliver trusted datasets. Supporting Infrastructure (Secondary Focus) Orchestrate pipelines using a workflow orchestration framework (e.g., Airflow or equivalent). Package and run data workloads using Docker and deploy them on Kubernetes. Use autoscaling and Spot / Preemptible compute for efficient pipeline execution. Build CI/CD automation for data pipelines. Use Infrastructure as Code only to provision and manage the infrastructure required to run pipelines. We expect you to have: 8+ years of experience as a Data Engineer, primarily focused on building data pipelines. 6+ years of hands-on experience with Python and SQL. 3+ years of experience running workloads on Kubernetes. Strong understanding of stateless system design and idempotent data processing. Experience building and operating data pipelines in cloud environments. Experience with workflow orchestration frameworks. Strong Linux fundamentals and production debugging skills. Working knowledge of spoken and written English It will be an added bonus if you have: Experience contributing to or working extensively with open-source software. Experience building data pipelines using Apache Spark or similar distributed processing frameworks. Experience building data pipelines that support machine learning workflows. Familiarity with cost-optimized data processing (e.g., Spot / Preemptible compute). Experience with relational and non-relational data stores. Experience working with large-scale or high-reliability data systems. Experience collaborating with strong Data Science and ML teams. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Based in the San Francisco Bay Area, or willing to relocate. The role We're looking for a scrappy, resourceful Developer Advocate who understands inference to help developers run open models in production on Token Factory, Nebius' high-performance inference platform. Every agent, copilot and AI product runs on an inference layer, and the developers choosing that layer have hard questions: Which open model should I run for this step? What does this cost per million tokens at my traffic? Why is my p99 latency spiking? Why does my multi-turn agent get slower and more expensive as the context grows? When do I move from a serverless endpoint to a dedicated one, and when does fine-tuning beat a longer prompt? Your job is to have credible, hands-on answers to those questions, and to show the work in public. You'll be embedded with the Token Factory product and engineering team in San Francisco. You'll be in their standups and bug bashes, you'll carry customer stories back to them, and you'll fix the docs gap yourself when you find one. You'll be equally at home in the Bay Area's AI-native community: the meetups our customers and partners host, and the open-source inference projects developers actually use. This is a hands-on, builder-first role. The content that matters here is less "how to call the API" and more "here's how a production inference stack is put together, and here are the tradeoffs." If you have strong opinions about serving open models and want a platform to prove them on, this role is for you. This role is based in the San Francisco Bay Area. The Token Factory team's center of gravity is in San Francisco and we expect this person to be part of the local community in person. Your responsibilities will include: Help developers and teams discover, evaluate and adopt Token Factory for production inference workloads, from a first API call on a serverless endpoint to dedicated endpoints and fine-tuned models. Build demos, benchmarks and reference architectures that make real tradeoffs concrete: which open model to use for which step, latency against cost per token, how context growth affects multi-turn agents, reliability of tool calling and structured outputs, and when fine-tuning or a custom speculator pays off versus a bigger model or a longer prompt. Show how Token Factory fits into the stack developers already use: OpenAI-compatible SDKs, agent frameworks, MCP and tool use, evals, embeddings and retrieval, and post-training. Be embedded with Token Factory product and engineering: join standups and bug bashes, test new models and features before launch, and make sure the right samples and docs exist on day zero of every release. Own the developer feedback loop. Gather what's breaking for builders, bring it back as concrete product input, and follow through until it ships or is explicitly declined. Represent Nebius in the Bay Area inference and open-model community: meetups, partner and customer events, open-source communities such as vLLM, SGLang and Ray, and developer conferences. Publish technical content and give talks that take developers from first hearing about Token Factory to running something on it, and partner with marketing to turn real builder stories into case studies. Work closely with the DevRel team and the Token Factory product marketing team on launches, community programs and content strategy. We expect you to have: Hands-on experience running open models in production or at scale through an inference provider or a serving stack, with a clear point of view on how you chose it and what broke. If you haven't tried Token Factory yet, we'll expect you to have done so before we talk. A working understanding of what happens under the hood of a serving engine like vLLM or SGLang: continuous batching, KV and prefix caching, quantization, speculative decoding, and how each shows up in latency and cost. You won't be tuning these on Token Factory, but you need to hold your own with the engineers who do. Current, first-hand knowledge of the open model ecosystem, including which models are worth recommending for which workloads and why. Comfort writing and reviewing code, reproducing issues, and shipping fixes to docs and samples yourself. A track record of building and explaining in public: talks, open-source contributions, writing, or an active technical presence on GitHub, X or LinkedIn. A DevRel title is not required. The ability to explain complex systems clearly to technical and non-technical audiences, and to be a credible technical partner to engineers, product managers and customers. Based in the San Francisco Bay Area, or willing to relocate. Candidates who come from forward-deployed engineering, solutions architecture, ML engineering or technical product roles are strongly encouraged to apply. It's a plus if you have: Hands-on experience with supervised fine-tuning, distillation or RL post-training of open models, and a view on when it beats a longer prompt or a bigger model. Contributions to open-source inference or distributed-compute projects such as vLLM, SGLang or Ray. Experience at an inference or GPU cloud provider, or as a heavy user of several. An existing audience that trusts your takes on models and infrastructure. #LI-LT1 Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $179,500 — $224,300 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We are looking for an Embedded Software Developer to design and implement the firmware and low-level software that powers our next-generation GPU and HPC platforms. This role will focus on embedded control, board management, telemetry, and hardware-firmware integration, ensuring that our systems operate reliably in high-density, mission-critical environments. Key Responsibilities • Design and implement embedded firmware for server management, telemetry, and control systems. • Maintain and enhance our custom OpenBMC firmware with new features and improvements • Enable real-time monitoring of power, thermal sensors, and hardware health. • Work closely with hardware engineers to validate firmware for existing and future platforms • Debug and optimize low-level drivers and protocols. • Contribute to long-term firmware architecture for GPU cluster reliability. Required Skills & Qualifications: • 5+ years in embedded systems or firmware development. • Proficiency in embedded Linux. • Hands-on experience with BMCs, microcontrollers, or SoC firmware. • Understanding of hardware bring-up and debugging • Languages: C, C++, Bash, Go, YAML • Firmware: OpenBMC, U-Boot, Linux Kernel • Interfaces: I2C, I3C, SPI, eSPI, UART, LPC • Protocols: SMBus, PCIe, PMBus, PECI • Build Systems: Meson, CMake • Descriptors & Formats: FRU, SMBIOS, ACPI, DMI Preferred: • Knowledge of the Yocto Project principles • Knowledge of systems and D-Bus principles • Proficiency in C++ • Good knowledge of C, sufficient for periodic work with Linux drivers and the U-Boot bootloader; • Experience in developing Linux drivers of any kind, especially those implementing sysfs and hwmon interfaces. • Experience with server BMC firmware IPMI, IPMB, KCS, SSIF, Redfish, PLDM • Knowledge of GPU/CPU telemetry frameworks (e.g., NVML, DCGM) • Exposure to firmware security (Secure Boot, signed firmware) • Experience with RAS (Reliability, Availability, Serviceability) • Background in high-performance computing or data center hardware. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $179,500 — $269,200 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. About the Role We are looking for a technically strong, hands-on QA Engineer to join our hardware team on-site at ODM factories in Taiwan. This is not a checklist job - we're looking for someone who enjoys digging deep into technical issues, investigating root causes, and taking ownership of complex hardware problems.You'll be the key person ensuring the quality of our servers and racks before they ship, but more importantly, you'll play a critical role in debugging failures, analyzing test data, and working closely with RnD, logistics, and factory teams to continuously improve the process and the product.This is a deeply technical role that blends hardware validation, manufacturing QA, and problem-solving - perfect for someone who understands how servers are built and tested, and wants to make sure every unit that leaves the factory is production-grade. What You'll Own Technical Investigation & Debugging Investigate complex problems (e.g., high GPU failure rate, power-related test failures), gather logs, run diagnostics, and escalate with context to RnD when needed. Drive root cause analysis across factory teams and internal engineering groups. Document findings and help define preventive actions for recurring problems. Act as the first line of technical escalation for hardware issues discovered during factory QA or internal testing.Engineering Support Participate in new platform bring-up sessions together with the visiting RnD teams during on-site trips to ODM labs. Provide technical support, coordination, and hands-on assistance during the bring-up process. Help ensure early-stage hardware behaves as expected, and escalate integration or platform issues to the relevant teams.On-Site Product QA Perform visual inspections of completed products (servers, racks) before packaging. Define and maintain QA checklists and inspection procedures tailored to different product lines. Verify inventory records at the factory against internal system data (part numbers, serials, configurations). Oversee the product packaging process for compliance with defined standards. Supervise pickup operations: ensure outbound trucks meet shipment conditions and schedules.Failure Rate Monitoring & Analytics Collect failure data from vendor-side burn-in and our own test systems. Analyze failure trends and estimate spare part needs for future datacenter deployments. Use dashboards and structured reporting to communicate insights with QA, engineering, and supply chain teams.Feedback Loop & Quality Improvement Gather and process feedback from datacenters on each delivered batch of equipment: * Report on packaging issues, impact sensor triggers, shipping anomalies. * Assess rack-level build quality: cabling, bracket alignment, labeling. * Log systemic hardware issues (design flaws, infant mortality, recurring failures). Forward the feedback to the teams: logistics, ODM partners, hardware RnD, QA.Test Infrastructure & Validation Assist with deployment and maintenance of test infrastructure on-site. Ensure Nebius post-manufacturing hardware validation tests run smoothly (uptime, monitoring, coordination with support team). Coordinate real-time issue escalation and basic triage with factory and internal teams.Local Insight & Communication Communicate relevant local risks and context (e.g., typhoons, holidays, factory-specific constraints) to our global logistics and hardware teams. Maintain productive relationships with factory staff, logistics providers, and internal stakeholders. Working Conditions & Tools During production peaks, issues may arise that require fast, hands-on debugging and resolution on-site. Flexibility is expected: you may need to stay late to investigate failures in freshly built batches or arrive early to verify and unblock outbound truck shipments. Rapid response and clear communication with engineering and factory teams are critical during these high-pressure periods. Occasional international travel may be expected to Nebius headquarters in Amsterdam or to datacenters in Europe and the US. Daily work tools involve: * Managing workflows and escalation via Jira * Writing and maintaining technical documentation in Confluence * Using Grafana dashboards for monitoring test environments and system health * Operating with several internal inventory and test control systems What You'll Bring Strong technical background in hardware or systems engineering, able to independently investigate and troubleshoot complex issues with server systems. 5+ years of experience in hardware QA, manufacturing supervision, or server validation. A strong background in R&D is a significant plus. Solid understanding of server and rack hardware: components, layout, cabling, power/cooling, diagnostics. Ability to read and interpret technical documentation (e.g., datasheets, system specs, debug manuals). Solid knowledge of electrical engineering fundamentals (e.g., power specs, grounding, signal integrity). Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is building fast, reliable, and cost-efficient inference services for frontier models. As a Senior Machine Learning Engineer on our Applied AI team, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, inference engines, serving architecture, and benchmarking, with a focus on improving latency, throughput, memory efficiency, GPU utilization, and cost per token while maintaining model quality and reliability. This is a hands-on role in which you will work on complex optimization projects, diagnose difficult serving problems, and deliver measurable improvements in production. Working closely with kernel and platform engineers, you will evaluate serving configurations, resolve performance and quality regressions, and optimize inference for real-world workloads, supported by reproducible benchmarks and safe production rollouts. Your responsibilities : Own optimization work for specific model families, customer endpoints, or serving backends. Run engine comparisons and recommend practical serving configurations for specific workloads. Debug model quality or performance regressions during production rollouts. Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems. Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV -cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. Build reproducible benchmark harnesses for TTFT , TPOT , tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations. Must-haves : Strong Python and PyTorch engineering skills. Hands-on experience deploying or optimizing LLM, VLM , or high-throughput transformer inference systems. Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems. Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs. Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams. Nice - to - have s : Experience with quantization-aware training, post-training quantization, FP8 , INT8 , INT4 , NVFP4 , MXFP4 , AWQ , GPTQ , SmoothQuant, or related techniques. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods. Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role. Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects. Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. #LI-BH3 Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $195,200 — $262,200 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role This role is for Nebius AI R&D, a team focused on applied research in AI. Our Physical AI research aims to build intelligent agents that can perceive, reason, and act in the physical world. Research areas include: Vision-language-action models for general-purpose robotic control Reinforcement and imitation learning from human demonstrations, simulation, and real-world experience Scalable collection, generation, and curation of multimodal embodied data Simulation, world models, and sim-to-real transfer Multimodal sensing, including vision, touch, force, and proprioception You will modify large foundation models and learning algorithms for robotic agents, prototype new capabilities in simulation, and validate promising approaches on real-world systems. The results will often lead to collaboration with adjacent research, infrastructure, and engineering teams, where findings are scaled and applied in practice. We are currently looking for senior- and staff-level ML engineers to work on research in areas such as: Vision-language-action models and multimodal foundation models for robotics Reinforcement learning, imitation learning, and learning from demonstrations Scalable acquisition and generation of human, robot, and simulated interaction data World models, planning, and model-based control Sim-to-real transfer, domain adaptation, and robust policy evaluation Dexterous manipulation, whole-body control, and general-purpose robotic agents Some examples of what your responsibilities might include are: Designing, implementing, training, and evaluating large models and learning algorithms for robotic agents Developing vision-language-action architectures that connect multimodal perception and language understanding with physical control Investigating reinforcement learning and imitation learning methods for sparse, delayed, or difficult-to-verify objectives Building scalable methods for incorporating demonstrations, teleoperation data, video, simulation trajectories, and autonomous robot experience into foundation models Designing capture methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning Developing simulation environments and conducting sim-to-real experiments on physical robotic platforms Exploring planning, guided generation, and search over action trajectories Prototyping new capabilities in areas such as dexterous manipulation, mobile manipulation, and whole-body control Writing robust research software and distributed training infrastructure that enable rapid experimentation Collaborating with research and engineering teams to translate promising ideas into reliable real-world systems Communicating results through technical reports, open-source releases, demonstrations, and research publications We expect you to have: A profound understanding of the theoretical foundations of machine learning, reinforcement learning, or robot learning Deep expertise in at least one relevant area, such as reinforcement learning, imitation learning, multimodal generative modeling, computer vision, robotics, planning, or control Experience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models Substantial experience training large models across multiple computational nodes Strong software engineering and algorithm-design skills; we primarily use Python Deep experience with a modern deep learning framework; we primarily use JAX Experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor Ability to formulate meaningful research questions, design experiments that test clear hypotheses, and draw defensible conclusions Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation Strong communication and leadership abilities, including the ability to collaborate across research and engineering disciplines Ability to document research findings clearly and contribute to technical reports or research publications Nice to have: Experience working with real-world robots and robotic simulation environments Experience with dexterous manipulation, whole-arm manipulation, mobile manipulation, or humanoid robotics Experience with multimodal sensing, including tactile, force-torque, depth, and proprioceptive signals Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation Experience developing or post-training vision-language models, vision-language-action models, or video and world models Experience with deep reinforcement learning techniques such as offline RL , actor-critic methods, PPO , reward modeling, preference learning, or model-based RL Familiarity with robotics tools and simulators such as MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS , or equivalent systems Knowledge of scalable training techniques such as FSDP or ZeRO , FlashAttention, mixed-precision training, quantization, and distributed checkpointing A PhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience A track record of impactful publications, open-source contributions, or deployed robotic systems Experience engineering large distributed data-processing, simulation, or model-training systems A record of building and delivering products or research prototypes in a dynamic, startup-like environment Passion for moving research from controlled experiments to capable, reliable real-world robotic systems Excellent command of English, with strong technical writing, presentation, and communication skills Proficiency in contemporary software engineering practices, including version control, testing, code review, and CI/CD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role This role is for Nebius AI R&D, a team focused on applied research in AI. Our Physical AI research aims to build intelligent agents that can perceive, reason, and act in the physical world. Research areas include: Vision-language-action models for general-purpose robotic control Reinforcement and imitation learning from human demonstrations, simulation, and real-world experience Scalable collection, generation, and curation of multimodal embodied data Simulation, world models, and sim-to-real transfer Multimodal sensing, including vision, touch, force, and proprioception You will modify large foundation models and learning algorithms for robotic agents, prototype new capabilities in simulation, and validate promising approaches on real-world systems. The results will often lead to collaboration with adjacent research, infrastructure, and engineering teams, where findings are scaled and applied in practice. We are currently looking for senior- and staff-level ML engineers to work on research in areas such as: Vision-language-action models and multimodal foundation models for robotics Reinforcement learning, imitation learning, and learning from demonstrations Scalable acquisition and generation of human, robot, and simulated interaction data World models, planning, and model-based control Sim-to-real transfer, domain adaptation, and robust policy evaluation Dexterous manipulation, whole-body control, and general-purpose robotic agents Some examples of what your responsibilities might include are: Designing, implementing, training, and evaluating large models and learning algorithms for robotic agents Developing vision-language-action architectures that connect multimodal perception and language understanding with physical control Investigating reinforcement learning and imitation learning methods for sparse, delayed, or difficult-to-verify objectives Building scalable methods for incorporating demonstrations, teleoperation data, video, simulation trajectories, and autonomous robot experience into foundation models Designing capture methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning Developing simulation environments and conducting sim-to-real experiments on physical robotic platforms Exploring planning, guided generation, and search over action trajectories Prototyping new capabilities in areas such as dexterous manipulation, mobile manipulation, and whole-body control Writing robust research software and distributed training infrastructure that enable rapid experimentation Collaborating with research and engineering teams to translate promising ideas into reliable real-world systems Communicating results through technical reports, open-source releases, demonstrations, and research publications We expect you to have: A profound understanding of the theoretical foundations of machine learning, reinforcement learning, or robot learning Deep expertise in at least one relevant area, such as reinforcement learning, imitation learning, multimodal generative modeling, computer vision, robotics, planning, or control Experience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models Substantial experience training large models across multiple computational nodes Strong software engineering and algorithm-design skills; we primarily use Python Deep experience with a modern deep learning framework; we primarily use JAX Experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor Ability to formulate meaningful research questions, design experiments that test clear hypotheses, and draw defensible conclusions Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation Strong communication and leadership abilities, including the ability to collaborate across research and engineering disciplines Ability to document research findings clearly and contribute to technical reports or research publications Nice to have: Experience working with real-world robots and robotic simulation environments Experience with dexterous manipulation, whole-arm manipulation, mobile manipulation, or humanoid robotics Experience with multimodal sensing, including tactile, force-torque, depth, and proprioceptive signals Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation Experience developing or post-training vision-language models, vision-language-action models, or video and world models Experience with deep reinforcement learning techniques such as offline RL , actor-critic methods, PPO , reward modeling, preference learning, or model-based RL Familiarity with robotics tools and simulators such as MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS , or equivalent systems Knowledge of scalable training techniques such as FSDP or ZeRO , FlashAttention, mixed-precision training, quantization, and distributed checkpointing A PhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience A track record of impactful publications, open-source contributions, or deployed robotic systems Experience engineering large distributed data-processing, simulation, or model-training systems A record of building and delivering products or research prototypes in a dynamic, startup-like environment Passion for moving research from controlled experiments to capable, reliable real-world robotic systems Excellent command of English, with strong technical writing, presentation, and communication skills Proficiency in contemporary software engineering practices, including version control, testing, code review, and CI/CD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role This role is for Nebius AI R&D, a team focused on applied research in AI. Our Physical AI research aims to build intelligent agents that can perceive, reason, and act in the physical world. Research areas include: Vision-language-action models for general-purpose robotic control Reinforcement and imitation learning from human demonstrations, simulation, and real-world experience Scalable collection, generation, and curation of multimodal embodied data Simulation, world models, and sim-to-real transfer Multimodal sensing, including vision, touch, force, and proprioception You will modify large foundation models and learning algorithms for robotic agents, prototype new capabilities in simulation, and validate promising approaches on real-world systems. The results will often lead to collaboration with adjacent research, infrastructure, and engineering teams, where findings are scaled and applied in practice. We are currently looking for senior- and staff-level ML engineers to work on research in areas such as: Vision-language-action models and multimodal foundation models for robotics Reinforcement learning, imitation learning, and learning from demonstrations Scalable acquisition and generation of human, robot, and simulated interaction data World models, planning, and model-based control Sim-to-real transfer, domain adaptation, and robust policy evaluation Dexterous manipulation, whole-body control, and general-purpose robotic agents Some examples of what your responsibilities might include are: Designing, implementing, training, and evaluating large models and learning algorithms for robotic agents Developing vision-language-action architectures that connect multimodal perception and language understanding with physical control Investigating reinforcement learning and imitation learning methods for sparse, delayed, or difficult-to-verify objectives Building scalable methods for incorporating demonstrations, teleoperation data, video, simulation trajectories, and autonomous robot experience into foundation models Designing capture methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning Developing simulation environments and conducting sim-to-real experiments on physical robotic platforms Exploring planning, guided generation, and search over action trajectories Prototyping new capabilities in areas such as dexterous manipulation, mobile manipulation, and whole-body control Writing robust research software and distributed training infrastructure that enable rapid experimentation Collaborating with research and engineering teams to translate promising ideas into reliable real-world systems Communicating results through technical reports, open-source releases, demonstrations, and research publications We expect you to have: A profound understanding of the theoretical foundations of machine learning, reinforcement learning, or robot learning Deep expertise in at least one relevant area, such as reinforcement learning, imitation learning, multimodal generative modeling, computer vision, robotics, planning, or control Experience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models Substantial experience training large models across multiple computational nodes Strong software engineering and algorithm-design skills; we primarily use Python Deep experience with a modern deep learning framework; we primarily use JAX Experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor Ability to formulate meaningful research questions, design experiments that test clear hypotheses, and draw defensible conclusions Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation Strong communication and leadership abilities, including the ability to collaborate across research and engineering disciplines Ability to document research findings clearly and contribute to technical reports or research publications Nice to have: Experience working with real-world robots and robotic simulation environments Experience with dexterous manipulation, whole-arm manipulation, mobile manipulation, or humanoid robotics Experience with multimodal sensing, including tactile, force-torque, depth, and proprioceptive signals Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation Experience developing or post-training vision-language models, vision-language-action models, or video and world models Experience with deep reinforcement learning techniques such as offline RL , actor-critic methods, PPO , reward modeling, preference learning, or model-based RL Familiarity with robotics tools and simulators such as MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS , or equivalent systems Knowledge of scalable training techniques such as FSDP or ZeRO , FlashAttention, mixed-precision training, quantization, and distributed checkpointing A PhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience A track record of impactful publications, open-source contributions, or deployed robotic systems Experience engineering large distributed data-processing, simulation, or model-training systems A record of building and delivering products or research prototypes in a dynamic, startup-like environment Passion for moving research from controlled experiments to capable, reliable real-world robotic systems Excellent command of English, with strong technical writing, presentation, and communication skills Proficiency in contemporary software engineering practices, including version control, testing, code review, and CI/CD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Your responsibilities will include: Develop and maintain Python-based API integrations between our HR information system (HRIS) and business systems, including finance, travel, expense management, and recruiting platforms. Build and maintain cloud-native services, web applications, and containerized workloads on Microsoft Azure. Implement automation for system workflows, onboarding/offboarding, access-related processes, and data synchronization. Create and maintain deployment pipelines and Infrastructure-as-Code with Terraform. Own integrations through implementation, testing, deployment, and production support; respond to alerts and troubleshoot issues across APIs, Azure services, and data sources within agreed SLAs. Implement safeguards for sensitive employee data, including validation, audit trails, and controls around mass changes. Work with business stakeholders to clarify requirements and maintain technical documentation as part of each change. We expect you to have: 3+ years of integration or backend development experience, including hands-on experience deploying and supporting production workloads in Microsoft Azure. Practical experience with Azure compute and integration services. Our stack includes Azure Functions, App Service, Container Apps, Container Instances, Service Bus, and API Management. Practical Python development skills, including maintaining and debugging existing services and automations. Experience integrating REST APIs, webhooks, and Microsoft Graph API, with an understanding of pagination, rate limits, retries, and idempotency. Experience working with CI/CD pipelines and Terraform for Azure deployments. Good understanding of OAuth 2.0, Microsoft Entra ID, and managed identities for secure authentication and service-to-service access. Experience designing and working with relational databases (PostgreSQL, MSSQL, etc.). Experience with automated testing and diagnosing production issues through logs, monitoring, and alerts. It will be an added bonus if you have: Experience integrating HRIS or other business systems, such as ERP, expense management, or recruiting platforms. Azure certifications (AZ-204, AZ-900, AZ-104). Hands-on experience with Application Insights and Log Analytics. Experience using AI-assisted development tools, with the ability to critically evaluate their output. #LI-RK1 Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The Product In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible. We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale. The Role We are looking for a Senior Software Engineer to work on the content acquisition and crawling infrastructure of a novel search engine tailored for agentic AI consumption. In this role, you will focus on building systems that discover, fetch, and continuously refresh content from the open web and other large-scale data sources. You will design distributed crawling, scheduling, and ingestion infrastructure capable of operating at internet scale while balancing coverage, freshness, resource efficiency, and reliability. You will work on systems that process billions of URLs, manage high-throughput data flows, and ensure that high-quality content is consistently available to downstream indexing and retrieval systems. In this position, your responsibility will be to: Design, implement, and operate web-scale crawling systems for acquiring content from the internet Build ingestion workflows for internal and external data sources, including crawlers, structured feeds, and partner integrations Develop crawl scheduling, prioritisation, recrawl policies, and freshness strategies Build systems for URL discovery, deduplication, content extraction, and crawl orchestration Ensure reliable operation of crawling infrastructure under high-throughput conditions Define observability and quality metrics for crawl coverage, freshness, throughput, and content quality Monitor resource usage, bandwidth consumption, and infrastructure cost Collaborate with indexing and ML teams to ensure acquired content meets retrieval and ranking requirements Enable safe experimentation with crawling strategies and content acquisition policies You may be a good fit if you: 5+ years of experience building backend or distributed systems Strong Go or C++ expertise Experience with large-scale distributed systems (10k+ RPS, billions of URLs, high-throughput pipelines) Understanding of web protocols (HTTP, DNS, TLS), crawling, scraping, and content extraction Experience operating production systems and debugging failures in distributed environments Strong understanding of scalability, fault tolerance, and resource management Strong candidates may also have experience with: Web crawling Building streaming data pipelines and event-driven systems Kafka, Pulsar, NATS, RabbitMQ, or similar messaging platforms Designing distributed schedulers, queues, and asynchronous processing systems Spark, Flink, Beam, or MapReduce Ad tech, social networks, search engines, or other large-scale content platforms We conduct coding interviews as part of the process. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.