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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 The Data Engineering team builds and operates the data platform that powers analytics, business intelligence, operational reporting, and data-driven products across Nebius. We ingest data from internal and external systems, develop reliable transformation pipelines and data models, and make trusted datasets available to business and product teams. We are looking for a Senior Data Engineer to own substantial parts of our data platform and deliver complex data products end to end. You will turn ambiguous business needs into pragmatic technical solutions, make architecture and implementation trade-offs, and improve the reliability, scalability, and usability of our data ecosystem. You will work closely with product, platform, and business teams, helping them use data effectively while ensuring that our systems remain maintainable and trustworthy as Nebius grows. Your responsibilities : Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business and analytical requirements into scalable data models, reliable data products, and clear technical plans. Design and evolve data architecture, storage, processing, and orchestration patterns for large-scale workloads. Improve data quality, observability, lineage, and incident response for critical datasets and pipelines. Investigate and resolve challenging performance, reliability, and data-correctness issues in production. Establish reusable tools, conventions, and automation that improve engineering productivity and reduce operational risk. Work with product teams and business stakeholders to define data contracts, priorities, and success criteria. Contribute to technical direction through design reviews, thoughtful trade-offs, and documentation. Support and mentor other engineers through reviews, pairing, and knowledge sharing. Participate in the on-call rotation and take ownership of improving the operational health of the systems you support. Must-haves : 5+ years of experience in data engineering, backend engineering, or a related role; or equivalent experience delivering and operating production data systems. Proven experience independently delivering complex data pipelines or data-platform capabilities from problem definition through production operation. Strong Python and SQL skills, including writing maintainable production code and optimizing non-trivial queries. Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster. Strong understanding of data modeling, including designing maintainable analytical models and data contracts for multiple consumers. Solid knowledge of data architectures and storage systems, including the trade-offs between different processing and storage approaches. Experience designing for reliability: testing, monitoring, data-quality validation, alerting, debugging, and incident resolution. Ability to make technical decisions under ambiguity, explain trade-offs clearly, and collaborate effectively with engineers and non-technical stakeholder Nice - to - have s : Experience with real-time or event-driven data platforms and streaming technologies. Experience building or operating cloud-native services with Docker and Kubernetes. Familiarity with Infrastructure as Code, particularly Terraform. Experience with data governance, access control, privacy, and compliance requirements such as GDPR or SOC 2. Experience with data observability and quality tools or frameworks, such as Great Expectations. Experience improving engineering standards through shared libraries, platform tooling, technical documentation, or mentoring. 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.

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 The Data Engineering team builds and operates the data platform that powers analytics, business intelligence, operational reporting, and data-driven products across Nebius. We ingest data from internal and external systems, develop reliable transformation pipelines and data models, and make trusted datasets available to business and product teams. We are looking for a Senior Data Engineer to own substantial parts of our data platform and deliver complex data products end to end. You will turn ambiguous business needs into pragmatic technical solutions, make architecture and implementation trade-offs, and improve the reliability, scalability, and usability of our data ecosystem. You will work closely with product, platform, and business teams, helping them use data effectively while ensuring that our systems remain maintainable and trustworthy as Nebius grows. Your responsibilities : Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business and analytical requirements into scalable data models, reliable data products, and clear technical plans. Design and evolve data architecture, storage, processing, and orchestration patterns for large-scale workloads. Improve data quality, observability, lineage, and incident response for critical datasets and pipelines. Investigate and resolve challenging performance, reliability, and data-correctness issues in production. Establish reusable tools, conventions, and automation that improve engineering productivity and reduce operational risk. Work with product teams and business stakeholders to define data contracts, priorities, and success criteria. Contribute to technical direction through design reviews, thoughtful trade-offs, and documentation. Support and mentor other engineers through reviews, pairing, and knowledge sharing. Participate in the on-call rotation and take ownership of improving the operational health of the systems you support. Must-haves : 5+ years of experience in data engineering, backend engineering, or a related role; or equivalent experience delivering and operating production data systems. Proven experience independently delivering complex data pipelines or data-platform capabilities from problem definition through production operation. Strong Python and SQL skills, including writing maintainable production code and optimizing non-trivial queries. Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster. Strong understanding of data modeling, including designing maintainable analytical models and data contracts for multiple consumers. Solid knowledge of data architectures and storage systems, including the trade-offs between different processing and storage approaches. Experience designing for reliability: testing, monitoring, data-quality validation, alerting, debugging, and incident resolution. Ability to make technical decisions under ambiguity, explain trade-offs clearly, and collaborate effectively with engineers and non-technical stakeholder Nice - to - have s : Experience with real-time or event-driven data platforms and streaming technologies. Experience building or operating cloud-native services with Docker and Kubernetes. Familiarity with Infrastructure as Code, particularly Terraform. Experience with data governance, access control, privacy, and compliance requirements such as GDPR or SOC 2. Experience with data observability and quality tools or frameworks, such as Great Expectations. Experience improving engineering standards through shared libraries, platform tooling, technical documentation, or mentoring. 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.

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 The Data Engineering team builds and operates the data platform that powers analytics, business intelligence, operational reporting, and data-driven products across Nebius. We ingest data from internal and external systems, develop reliable transformation pipelines and data models, and make trusted datasets available to business and product teams. We are looking for a Senior Data Engineer to own substantial parts of our data platform and deliver complex data products end to end. You will turn ambiguous business needs into pragmatic technical solutions, make architecture and implementation trade-offs, and improve the reliability, scalability, and usability of our data ecosystem. You will work closely with product, platform, and business teams, helping them use data effectively while ensuring that our systems remain maintainable and trustworthy as Nebius grows. Your responsibilities : Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business and analytical requirements into scalable data models, reliable data products, and clear technical plans. Design and evolve data architecture, storage, processing, and orchestration patterns for large-scale workloads. Improve data quality, observability, lineage, and incident response for critical datasets and pipelines. Investigate and resolve challenging performance, reliability, and data-correctness issues in production. Establish reusable tools, conventions, and automation that improve engineering productivity and reduce operational risk. Work with product teams and business stakeholders to define data contracts, priorities, and success criteria. Contribute to technical direction through design reviews, thoughtful trade-offs, and documentation. Support and mentor other engineers through reviews, pairing, and knowledge sharing. Participate in the on-call rotation and take ownership of improving the operational health of the systems you support. Must-haves : 5+ years of experience in data engineering, backend engineering, or a related role; or equivalent experience delivering and operating production data systems. Proven experience independently delivering complex data pipelines or data-platform capabilities from problem definition through production operation. Strong Python and SQL skills, including writing maintainable production code and optimizing non-trivial queries. Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster. Strong understanding of data modeling, including designing maintainable analytical models and data contracts for multiple consumers. Solid knowledge of data architectures and storage systems, including the trade-offs between different processing and storage approaches. Experience designing for reliability: testing, monitoring, data-quality validation, alerting, debugging, and incident resolution. Ability to make technical decisions under ambiguity, explain trade-offs clearly, and collaborate effectively with engineers and non-technical stakeholder Nice - to - have s : Experience with real-time or event-driven data platforms and streaming technologies. Experience building or operating cloud-native services with Docker and Kubernetes. Familiarity with Infrastructure as Code, particularly Terraform. Experience with data governance, access control, privacy, and compliance requirements such as GDPR or SOC 2. Experience with data observability and quality tools or frameworks, such as Great Expectations. Experience improving engineering standards through shared libraries, platform tooling, technical documentation, or mentoring. 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.

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. 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. 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 Token Factory is a part of Nebius Cloud, one of the world’s largest GPU clouds, running tens of thousands of GPUs. We are building an inference & fine-tuning platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to train & deploy at massive scale. Some directions we currently working on and which you can be a part of: Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency. Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation. Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware We expect you to have: A profound understanding of theoretical foundations of machine learning and reinforcement learning. Deep expertise in modern deep learning for language processing and generation Experience with training large models on multiple computational nodes Reasonable understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.) Strong software engineering skills (we mostly use Python) Deep experience with modern deep learning frameworks (we use JAX) Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing Strong communication and leadership abilities Nice to have: Previous experience working with language models or other similar NLP technologies. Familiarity with important ideas in LLM space, such as MHA, RoPE, ZeRO/FSDP, Flash Attention, quantization A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment. Strong engineering skills, including experience in developing large distributed systems or high-load web services. Open-source projects that showcase your engineering prowess Excellent command of the English language, alongside superior writing, articulation, and communication skills. 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.