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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. We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search. Your responsibilities: Design, train, and deploy ML models for retrieval, reranking, and search relevance in production Build and optimise embedding-based indexing and large-scale retrieval systems Develop models supporting crawling, data selection, and content understanding Define and improve quality metrics for agent-native search and build evaluation pipelines Work on systems operating at very large scale, including high-throughput query workloads Collaborate closely with engineering teams to integrate ML models into production services Analyse performance trade-offs across latency, quality, and cost Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems Contribute to product and architectural decisions in a fast-moving environment Must-haves: 5+ years of experience in software engineering or applied machine learning Strong programming skills in Python, Go, or C++ Proven experience deploying ML models in production systems Hands-on experience with retrieval, ranking, recommendation, or similar ML problems Strong understanding of machine learning and modern deep learning techniques Experience working with large-scale data systems and high-throughput environments Ability to design evaluation frameworks and define meaningful model metrics Product-oriented mindset with a focus on impact and iteration Strong problem-solving skills and ability to work in a distributed team Nice-to-haves: Experience with search systems or large-scale information retrieval Familiarity with embeddings, transformers, and modern NLP systems Experience working on LLM-powered or agent-based systems Contributions to open-source projects, technical publications, or conference talks Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability 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 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 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 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 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 Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering. A Senior Machine Learning Engineer owns substantial ML work end to end. They can translate an ambiguous capability goal into concrete experiments, implement and debug training and RL recipes, build the supporting data and systems, and deliver measurable improvements in model quality, experiment throughput, and reliability. They are deeply hands-on and can independently debug both model-behavior failures and distributed training failures. Your responsibilities : Design and run model-training and post-training experiments, including SFT , continued pretraining, preference optimization ( DPO /IPO/ KTO ), and RL methods such as RLHF / RLAIF , PPO , and GRPO . Build reward functions, judge models, verifiers, task environments, and evaluation sets for reasoning, coding, tool use, and agentic workflows. Create synthetic data and data pipelines, including teacher-student generation, self-play, rejection sampling, filtering, and quality scoring. Analyze model-behavior failures and turn them into targeted data, reward, or algorithm improvements. Build and maintain distributed training and RL infrastructure using frameworks such as Megatron- LM , DeepSpeed, PyTorch FSDP /DTensor, Ray, verl, slime, AReaL, or OpenRLHF. Implement and debug parallelism strategies (tensor, pipeline, sequence/context, expert, and data parallelism) and build reliable rollout, reward-serving, checkpointing, and experiment-orchestration components. Profile and improve GPU utilization, memory usage, communication efficiency, training throughput, and inference/serving performance. Design rigorous evaluations and ablations for capability, instruction following, reasoning, tool use, safety, and regression risk. Write clear experiment plans, design docs, benchmark reports, and runbooks, and partner across research and platform teams. Must-haves : Strong Python and PyTorch engineering skills, with the ability to move quickly from idea to experiment to working system. Hands-on experience across at least two of: model training, post-training/ RL , applied modeling, data pipelines, or large-scale ML systems. Ability to design rigorous experiments with baselines, ablations, metrics, and failure analysis. Practical understanding of modern LLM behavior, instruction tuning, preference optimization, and evaluation challenges. Practical understanding of transformer training bottlenecks, memory pressure, communication overhead, and checkpointing. Ability to reason quantitatively about model quality, throughput, utilization, reliability, cost, and research velocity. Strong communication skills and ability to collaborate with researchers, engineers, and leadership. Nice - to - have s : Experience with LLM post-training, RL , agents, reward modeling, synthetic data, or model evaluation. Experience with RL frameworks or pipelines such as verl, slime, AReaL, OpenRLHF, TRL , or custom PPO / GRPO / RLHF systems. Experience with Megatron- LM , DeepSpeed, PyTorch FSDP /DTensor, Ray, Slurm, or Kubernetes on large GPU clusters. Familiarity with NCCL , CUDA , Triton, Nsight, InfiniBand/ RDMA , and H100/H200/B200 clusters, or with model serving and inference optimization. Publications, open-source contributions, or production impact in LLM post-training, RL , reasoning, coding models, synthetic data, distributed training, or evaluation. Experience designing agent environments, tool-use tasks, or verifier-based rewards. 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 $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 The Field CTO, Media & Entertainment AI Infrastructure is a senior engineering leader who will initially operate as an individual contributor at the intersection of deep AI infrastructure engineering and the business of media and entertainment, with a clear path to building and leading a team as the business scales. You will work alongside CTOs and engineering leaders at studio groups, VFX houses, gaming studios, agency holding companies, and generative AI model companies. Your role is not to pitch them; it is to build with them. You will translate their most complex infrastructure problems into engineered solutions, own the technical relationship with our most strategic ISV and cloud-native partners, and use what you learn in the field to directly influence the M&E product roadmap alongside Nebius's global Head of Product and Head of Engineering. This is a ground-floor opportunity to define what AI-native infrastructure looks like for an industry in the middle of a fundamental transformation. You are welcome to work remotely from the United States (SF Bay Area or NYC preferred) Travel expectations 10% - 20% for executive meetings and key industry events. Your responsibilities will include : AI Infrastructure Architecture Own the Technical Blueprint: Personally architect the infrastructure solutions for our most strategic M&E partnerships , studio-scale content production pipelines, agency data consolidation plays, generative AI model deployments. These architectures must be engineered to survive real-world scale, not just pass a POC. The Physics to P&L Narrative: Fluently demonstrate to executive stakeholders how infrastructure decisions , data lake locality, storage tiering, inference optimization, directly impact their business model and operability. Forensic Requirement Gathering Deconstruct the Bottleneck: Go beyond the stated problem to find the technical truth. Translate vague business goals (e.g., “We need lower rendering costs”) into precise engineering requirements (e.g., “We need to optimize the inference batch size on L40s to reduce cost-per-token by 30%”). Map the Transition: Identify exactly where a customer sits on the curve from legacy service bureau to AI-native tech platform and prescribe the specific infrastructure intervention needed to move them forward. ISV & Partner Technical Strategy Build and Validate the Integration Layer: Identify, engage, and technically validate relationships with the most critical ISVs in the media and entertainment landscape, from rendering and VFX toolchains to generative AI platforms. Define the Standard: It is not enough to support these tools. You will define the reference architectures for how they run best on Nebius infrastructure, and work directly with ISV engineering teams to build and publish those standards. Decide What’s Worth Doing: In partnership with the GM, evaluate ISV and partner opportunities on their technical merit and strategic leverage , and be equally rigorous about what not to pursue. Internal Technical Influence Shape the M&E Roadmap: Use forensic evidence from the field to prioritize and justify the M&E vertical roadmap. You will work directly with Nebius’s global Head of Product and Head of Engineering to translate partner and customer needs into product direction. Lead the M&E Product Summit: Chair a quarterly summit with Core Engineering leadership, using field evidence to drive roadmap decisions and maintain vertical momentum. We expect you to have : 12+ years of experience in cloud infrastructure, platform engineering, distributed systems, or a closely related technical domain. Executive Presence: Capable of commanding a room of engineers and presenting a layered technical roadmap to a C-Suite. You have operated at the top-to-top level , your counterparts are CTOs and VPs of Engineering. Builder Mentality: This role begins as a hands-on individual contributor position. You will architect and ship solutions alongside partners and build the assets (reference architectures, integration playbooks, technical frameworks) that make Nebius's M&E infrastructure strategy defensible and scalable. As the business case develops, this role is explicitly designed to grow into a team-building and leadership function, with the expectation that you will recruit, shape, and run that team. You may have come from a startup, run your own company, or operated within a large org in a way that felt like building from zero, and you know how to transition from maker to multiplier. Product-Minded: Experience defining a platform strategy, not just executing tickets. You are comfortable telling a customer “No” when a request creates technical debt, and proposing a better alternative. Ambiguity Tolerance: You thrive in environments where requirements are evolving. You do not wait for a roadmap; you build it. Forensic Mindset: You are not satisfied with surface-level answers. You dig into the kernel, the logs, and the P&L to find the truth. AI Infrastructure Expertise Mastery of the Stack: Expert-level, production-grade knowledge of GPU architectures (H100, L40s), Kubernetes orchestration including Soperator, high-performance and parallel file systems (e.g., Lustre, WEKA), data lake architecture, and networking constraints (InfiniBand/Ethernet). Inference Optimization: You understand the nuances of model serving , batch sizes, quantization, KV caching, latency tradeoffs , and can architect solutions for both massive throughput and real-time (sub-50ms) demands. Domain Context: Media & Entertainment Industry Fluency: You have operated within the M&E ecosystem and can speak to the infrastructure implications across the following sub-sectors: Gaming Multimodal Generative Models AdTech VFX / Content Production Non-negotiable: Candidates without deep, production-grade working knowledge of GPU infrastructure and inference-based solutioning will not be considered. This is a hard requirement, not a preference. AI Infrastructure Expertise Mastery of the Stack: You must be an expert in the physics of AI infrastructure. This includes deep, production-grade knowledge of GPU architectures (H100 vs. L40s), Kubernetes orchestration (K8s), high-performance storage parallel file systems (e.g., Lustre, WEKA), and networking constraints (InfiniBand/Ethernet). Inference Optimization: You understand the nuances of model serving—batch sizes, quantization, KV caching, and latency tradeoffs—and can architect solutions for both massive throughput and real-time (sub-50ms) demands. It will be an added bonus if you have : Hands-on experience with VFX pipelines, studio-scale content production workflows, or the cloud tooling that powers them , render farm management, distributed simulation, asset pipeline tooling. You understand how content gets made, moved, and transformed at scale, and what infrastructure makes that possible. Strategic Empathy: The ability to distinguish between customers who need raw bare-metal access and those who need managed endpoints, and the wisdom to prescribe the right path. Key Employee Benefits : 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 $200,000 — $245,000 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. Why work at Nebius Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field. Where we work Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. Our teams bring together deep expertise across hardware, software, networking, data center infrastructure, and AI to build and operate the infrastructure behind large-scale GPU computing. The team You will join our Data Center Infrastructure organization, supporting the critical environments that power Nebius GPU clusters and AI cloud infrastructure. Our team works across the boundary between traditional IT infrastructure and the electrical, mechanical, and cooling systems that keep high-density compute environments online. We partner closely with Data Center IT, Network Engineering, infrastructure providers, colocation partners, and internal leadership to ensure our facilities deliver the capacity, resilience, and operational performance required by our customers. This is an opportunity to develop broad expertise across both IT and critical infrastructure while helping establish the operational standards that support Nebius as our North American data center footprint continues to scale. The role We are seeking a Critical Infrastructure Engineer to help ensure the availability, resilience, and operational readiness of the critical systems supporting Nebius data center IT infrastructure. The primary objective of this role is uptime . You will provide technical oversight across the electrical and mechanical infrastructure responsible for delivering reliable power and cooling to our GPU and IT environments. Rather than serving primarily as a maintenance technician, you will verify that critical infrastructure is operated safely, consistently, and in accordance with established SLAs, engineering standards, change-control procedures, and operational best practices. You will also act as an important bridge between IT infrastructure teams and electrical/mechanical specialists. The ideal candidate understands how servers, networking equipment, racks, and GPU systems operate inside a data center while also having enough exposure to critical facilities systems to understand—and challenge when necessary—the infrastructure supporting them. The position combines technical analysis, provider governance, change management, incident response, and hands-on familiarity with data center IT environments. Your responsibilities will include: Critical Infrastructure & Uptime Help ensure the availability and operational readiness of the electrical and mechanical infrastructure supporting production data halls and high-density GPU environments. Monitor critical infrastructure performance against contractual SLAs, operational requirements, and established reliability standards. Develop a strong understanding of the complete power and cooling path supporting IT equipment and identify conditions that could introduce operational risk. Review infrastructure capacity, redundancy, and operating conditions to ensure the environment can reliably support current and planned compute deployments. Identify infrastructure risks and work with service providers and internal teams to drive corrective actions before they impact production. Support infrastructure planning for data center expansions, capacity increases, and new GPU deployments. Power & Electrical Infrastructure Provide technical oversight of data center electrical infrastructure, including generator plants, automatic transfer switches (ATS), UPS systems, battery banks, switchgear, breakers, busbars, bus plugs, PDUs, and related power distribution equipment. Understand electrical distribution from facility-level infrastructure through rack-level delivery and IT equipment. Participate in technical reviews involving power capacity, electrical distribution, equipment sizing, redundancy, and infrastructure design. Work with electrical engineers and infrastructure providers to evaluate proposed changes and ensure appropriate engineering validation is completed before production implementation. Cooling & Mechanical Infrastructure Understand the cooling architecture supporting high-density GPU and IT environments, including water and glycol loops, rear-door heat exchangers (RDHx), evaporative systems, coolant distribution systems, facility water systems, dry coolers, and chillers. Evaluate how cooling infrastructure interacts with GPU systems and high-density racks to maintain required operating conditions. Partner with mechanical engineers and service providers to review system performance, capacity constraints, and proposed infrastructure changes. Identify potential thermal or cooling risks that could affect compute availability or future capacity. Provider Governance & Change Control Provide technical oversight of third-party critical infrastructure and colocation service providers. Ensure provider activities comply with Nebius policies, approved procedures, contractual SLAs, and operational requirements. Review and approve change requests involving critical infrastructure supporting production environments. Challenge incomplete or high-risk work plans and ensure appropriate testing, rollback procedures, risk analysis, and stakeholder communication are in place before work begins. Maintain strong governance around maintenance and infrastructure changes that could affect production availability. Hold service providers accountable for corrective actions, operational performance, and agreed service levels. Incident Response & Operational Risk Participate in critical infrastructure incidents and coordinate technical response with providers, Data Center IT, networking, and engineering teams. Support root-cause analysis following power, cooling, or infrastructure-related incidents. Review incident findings and ensure corrective and preventive actions are documented, assigned, and completed. Help develop and continuously improve emergency response procedures, escalation paths, change-control standards, and operational documentation. Identify recurring infrastructure risks and drive improvements that increase reliability and reduce the likelihood of customer impact. IT & Critical Infrastructure Integration Work closely with Data Center IT teams to understand how critical infrastructure conditions affect servers, networking equipment, GPU clusters, and other production systems. Apply practical knowledge of data center IT operations, including racks, servers, fiber, cabling, network equipment, and hardware deployment. Support cross-functional troubleshooting where the root cause may span IT equipment and facility infrastructure. Help create stronger operational alignment between IT infrastructure and electrical/mechanical teams. Reporting & Stakeholder Communication Translate complex infrastructure conditions, incidents, risks, and provider performance into clear information for technical and business leadership. Develop reports, dashboards, presentations, and operational analyses related to uptime, infrastructure performance, capacity, incidents, and service-provider performance. Participate in technical and leadership meetings as a subject-matter resource for data center critical infrastructure. Use operational data to identify trends, communicate risk, and drive measurable improvements in reliability and provider performance. We expect you to have: Experience working in data center, cloud infrastructure, colocation, critical facilities, or other mission-critical environments. Practical understanding of IT infrastructure, including servers, racks, networking equipment, structured cabling, and fiber. Working knowledge of data center electrical infrastructure such as UPS systems, generators, switchgear, PDUs, batteries, breakers, and power distribution. Exposure to data center mechanical and cooling systems, including chilled-water, glycol, liquid-cooling, or comparable thermal-management environments. Ability to understand how electrical and mechanical infrastructure directly impacts IT equipment availability and performance. Experience participating in infrastructure change management, incident response, operational risk management, or maintenance governance. Ability to review technical plans, ask detailed engineering questions, identify risk, and work effectively with electrical and mechanical subject-matter experts. Strong analytical skills with experience using Excel for reporting, data analysis, and operational metrics. Strong written and verbal communication skills with the ability to communicate effectively with engineers, vendors, service providers, and senior leadership. A proactive, ownership-driven approach with the ability to operate effectively in a high-availability production environment. Nice to have: Experience supporting high-density GPU, AI, HPC, or hyperscale data center environments. Experience with direct-to-chip liquid cooling or other advanced cooling technologies used for high-density compute. Experience managing colocation or third-party critical infrastructure providers against contractual SLAs. Familiarity with Tier III data center environments and high-availability infrastructure principles. Experience developing or implementing change-management, incident-response, or emergency-response procedures. Experience supporting infrastructure capacity planning, expansion projects, or new data center deployments. Relevant electrical, mechanical, data center, or critical facilities certifications. 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. Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. Join Nebius Today! 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 $85,000 — $140,000 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.