{"id":2243376,"url":"https://alion.io/job/nebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning","title":"Senior Machine Learning Engineer, Model Training and Reinforcement Learning","company":{"id":6364,"name":"Nebius","domain":"nebius.com","url":"https://alion.io/company/nebius","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":78,"open_postings":131,"ghost_share":0,"stale_share":0.87,"repost_share":0,"time_to_fill_p50_days":38,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":195200,"max":262200,"currency":"USD","period":"year","gross":null,"usd_annual":262200},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"DeepSpeed","optional":false},{"name":"DPO","optional":false},{"name":"FSDP","optional":false},{"name":"Function Calling","optional":false},{"name":"GRPO","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Post-training","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tool Use","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"InfiniBand","optional":true},{"name":"Kubernetes","optional":true},{"name":"NCCL","optional":true},{"name":"Reward Modeling","optional":true},{"name":"SLURM","optional":true},{"name":"Transformers","optional":true},{"name":"Triton","optional":true},{"name":"TRL","optional":true}],"status":"live","first_seen_at":"2026-07-22T22:52:25Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-13T17:21:32Z","board_verified":true,"closed_at":null,"days_open":82,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":82},"description":"About Nebius:\nNebius 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.\nBuilt by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\nListed 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.\nThe role \nNebius 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 RLexperiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RLpipelines, and production engineering.\nA 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 RLrecipes, 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.\nYour responsibilities: \nDesign and run model-training and post-training experiments, including SFT, continued pretraining, preference optimization (DPO/IPO/KTO), and RLmethods such as RLHF/RLAIF, PPO, and GRPO.\n\nBuild reward functions, judge models, verifiers, task environments, and evaluation sets for reasoning, coding, tool use, and agentic workflows.\n\nCreate synthetic data and data pipelines, including teacher-student generation, self-play, rejection sampling, filtering, and quality scoring.\n\nAnalyze model-behavior failures and turn them into targeted data, reward, or algorithm improvements.\n\nBuild and maintain distributed training and RLinfrastructure using frameworks such as Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, verl, slime, AReaL, or OpenRLHF.\n\nImplement and debug parallelism strategies (tensor, pipeline, sequence/context, expert, and data parallelism) and build reliable rollout, reward-serving, checkpointing, and experiment-orchestration components.\n\nProfile and improve GPU utilization, memory usage, communication efficiency, training throughput, and inference/serving performance.\n\nDesign rigorous evaluations and ablations for capability, instruction following, reasoning, tool use, safety, and regression risk.\n\nWrite clear experiment plans, design docs, benchmark reports, and runbooks, and partner across research and platform teams.\n\nMust-haves: \nStrong Python and PyTorch engineering skills, with the ability to move quickly from idea to experiment to working system.\n\nHands-on experience across at least two of: model training, post-training/RL, applied modeling, data pipelines, or large-scale ML systems.\n\nAbility to design rigorous experiments with baselines, ablations, metrics, and failure analysis.\n\nPractical understanding of modern LLM behavior, instruction tuning, preference optimization, and evaluation challenges.\n\nPractical understanding of transformer training bottlenecks, memory pressure, communication overhead, and checkpointing.\n\nAbility to reason quantitatively about model quality, throughput, utilization, reliability, cost, and research velocity.\n\nStrong communication skills and ability to collaborate with researchers, engineers, and leadership.\n\nNice-to-haves: \nExperience with LLM post-training, RL, agents, reward modeling, synthetic data, or model evaluation.\n\nExperience with RLframeworks or pipelines such as verl, slime, AReaL, OpenRLHF, TRL, or custom PPO/GRPO/RLHFsystems.\n\nExperience with Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, Slurm, or Kubernetes on large GPU clusters.\n\nFamiliarity with NCCL, CUDA, Triton, Nsight, InfiniBand/RDMA, and H100/H200/B200 clusters, or with model serving and inference optimization.\n\nPublications, open-source contributions, or production impact in LLM post-training, RL, reasoning, coding models, synthetic data, distributed training, or evaluation.\n\nExperience designing agent environments, tool-use tasks, or verifier-based rewards.\n\nKey employee benefits in the US:\nHealth insurance: 100% company-paid medical, dental, and vision coverage for employees and families.\n\n401(k) plan: Up to 4% company match with immediate vesting.\n\nParental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.\n\nRemote work reimbursement: Up to $85/month for mobile and internet.\n\nDisability & life insurance: Company-paid short-term, long-term and life insurance coverage.\n\nPay Transparency\nWe 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.\nBase Compensation Range\n$195,200—$262,200 USD\nBenefits & Perks:\nCompetitive compensation\nCareer growth and learning opportunities\nFlexibility and ownership\nCollaborative and innovative culture\nOpportunity to work on impactful AI projects\nInternational environment and talented teams\nWhat's it like to work at Nebius:\nFast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI \nEqual Opportunity Statement:\nNebius 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.\nApplicants 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. \nIf you need accommodations during the application process, please let us know.","description_format":"text","description_chars":6490,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Health insurance","Insurance coverage","Life insurance","Parental leave"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Commerce","Education","Data Centers & Colocation"],"lifecycle":[{"event":"open","at":"2026-10-11T01:05:42Z"}],"visa":[],"liveness":{"score":9,"band":"cold","label":"Long shot","p_open":1,"p_active":0.314,"p_room":0.28,"age_days":82,"expected_fill_days":38,"reasons":["conf:0","stale_co","win:tail","crowd:brand"],"computed_at":"2026-10-13T05:45:00Z"},"pay":{"stated_usd_annual":262200,"is_top_pay":true},"html_url":"https://alion.io/job/nebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning","json_url":"https://alion.io/job/nebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning.json","meta":{"generated_at":"2026-10-13T18:09:13Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":27739,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-14T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":6364},"rest":"https://alion.io/mcp/rest/get_company?id=6364"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fnebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fnebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fnebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/nebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fnebius-senior-machine-learning-engineer-model-training-and-reinforcement-learning"}]}