{"id":1930783,"url":"https://alion.io/job/institute-of-foundation-models-machine-learning-engineer-reinforcement-learning","title":"Machine Learning Engineer — Reinforcement Learning","company":{"id":99421,"name":"Institute of Foundation Models","domain":"ifm.ai","url":"https://alion.io/company/institute-of-foundation-models","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":95,"open_postings":42,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":482,"computed_at":"2026-11-05T05:54:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":450000,"currency":"USD","period":"year","gross":null,"usd_annual":450000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"GRPO","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NCCL","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"SGLang","optional":false},{"name":"SLURM","optional":false},{"name":"vLLM","optional":false},{"name":"CUDA","optional":true},{"name":"Pre-training","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-10-05T21:35:04Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-11-05T12:04:51Z","board_verified":true,"closed_at":null,"days_open":30,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":30},"description":"The Role\nWe’re looking for an RL infrastructure engineer to help extend and scale our end-to-end RL training systems. You’ll work side-by-side with world-class researchers and engineers to:\nExtend distributed training frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)\nIntegrate rollout generation, reward computation, trajectory processing, policy updates, and weight synchronization\nBuild robust config + launch systems across multi-node, multi-GPU clusters\nOwn experiment tracking, metrics logging, and job monitoring for external visibility\nImprove training system reliability, maintainability, and performance\nHands-on experience developing end-to-end RL infrastructure is required. Strong infrastructure and systems experience is what we value most.\nKey Responsibilities\nDistributed Framework Ownership - Extend or modify training frameworks (e.g., DeepSpeed, FSDP) to support new use cases and architectures.\nTraining & Inference Integration - Connect training workers with rollout generation engines (e.g., vLLM, SGLang), including trajectory exchange and policy-weight synchronization.\nLaunch Config & Debugging - Create and debug multi-node launch scripts with flexible batch sizes, parallelism strategies, and hardware targets.\nMetrics & Monitoring - Build systems for experiment tracking, job monitoring, and logging usable by collaborators and researchers.\nInfra Engineering - Write production-quality code and tests for ML infra in PyTorch or JAX; ensure reliability and maintainability at scale.\nRL Pipeline Development - Implement reward/verifier integration and trajectory processing, and validate log probabilities, token masks, and loss inputs with researchers.\nRL Execution & Recovery - Coordinate rollout and training workers, including checkpoint/restart and failure recovery; track policy versions and sample staleness when using asynchronous execution.\nQualifications\nMust-Haves:\n5+ years of experience in ML systems, infra, or distributed training\nExperience modifying distributed ML frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)\nStrong software engineering fundamentals (Python, systems design, testing)\nProven multi-node experience (e.g., Slurm, Kubernetes, Ray) and debugging skills (e.g., NCCL/GLOO)\nAbility to implement algorithms across GPUs/nodes based on mathematical specs\nExperience working on an ML platform/ infrastructure, and/or distributed inference optimization team\nExperience with large-scale machine learning workloads (strong ML fundamentals)\nHands-on experience developing an LLM RL pipeline, with ownership across rollout/inference and distributed training integration.\nWorking knowledge of policy optimization methods such as PPO or GRPO, sufficient to implement and debug sampling, log probabilities, and policy updates.\nNice-to-Haves:\nExposure to mixed-precision training (e.g., bf16, fp8) with accuracy validation\nFamiliarity with performance profiling, kernel fusion, or memory optimization\nOpen-source contributions or published research (MLSys, ICML, NeurIPS)\nCUDA or Triton kernel experience\nExperience with large-scale pre-training\nExperience building custom training pipelines at scale and modifying them for custom needs\nDeep familiarity with training infrastructure and performance tuning\nExperience with asynchronous RL, multi-turn or tool-using rollout environments, or custom reward/verifier 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