{"id":197262,"url":"https://alion.io/job/mistral-ai-research-platform-engineer-2","title":"Research Engineer, ML Platform","company":{"id":22,"name":"Mistral AI","domain":"mistral.ai","url":"https://alion.io/company/mistral-ai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":194,"ghost_share":0,"stale_share":0.005,"repost_share":0,"time_to_fill_p50_days":64,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":151000,"max_usd":293000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":508},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Go","optional":false},{"name":"Karpenter","optional":false},{"name":"Kubernetes","optional":false},{"name":"Kyverno","optional":false},{"name":"Mistral SDK","optional":false},{"name":"NCCL","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false}],"status":"live","first_seen_at":"2026-09-03T15:17:51Z","employer_posted_date":"2026-09-03","last_verified_at":"2026-11-01T06:01:09Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":58},"description":"About Mistral\nMistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.\nWe are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.\nThe Role\nThis role focuses on building and operating the ML platform that powers large-scale training, evaluation, and batch inference at Mistral AI. You will develop the infrastructure that enables researchers and engineers to run distributed GPU workloads reliably across clusters, hardware types, and regions.\nYou will work across the full ML lifecycle, from workload scheduling and capacity management to platform APIs, observability, and production operations. You will take ownership of critical systems and help turn complex infrastructure into reliable, self-service capabilities.\nWhat You Will Do\nBuild the ML Platform: Develop services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference.\n\nOrchestrate GPU Workloads: Build systems for queueing, admission control, quotas, priorities, preemption, and topology-aware placement.\n\nManage Compute Capacity: Improve how heterogeneous GPU resources are provisioned, allocated, and utilized across clusters.\n\nEnable Multi-Cluster Execution: Place workloads based on capacity, data locality, hardware requirements, and organizational priorities.\n\nImprove Researcher Experience: Create self-service workflows that make distributed workloads easy to launch, observe, debug, and reproduce.\n\nOptimize Performance: Improve GPU utilization, scheduling latency, workload startup time, throughput, and infrastructure efficiency.\n\nBuild for Reliability: Develop observability, failure recovery, capacity planning, and operational tooling for critical ML workloads.\n\nOperate What You Build: Participate in on-call rotations and troubleshoot issues across applications, schedulers, networking, storage, and GPU infrastructure.\n\nWhat We're Looking For\nHave 4+ years of experience in ML infrastructure, distributed systems, Kubernetes platform engineering, or a related field.\n\nAre proficient in Python or Go and comfortable working with production-grade distributed systems.\n\nHave strong Kubernetes knowledge, including controllers, operators, CRDs, scheduling, networking, storage, and resource management.\n\nUnderstand technologies such as Kueue, Karpenter, Volcano, and Kyverno, and the problems they address in workload scheduling, provisioning, and policy enforcement.\n\nUnderstand distributed ML workloads, including training, fine-tuning, evaluation, checkpointing, and batch inference.\n\nAre familiar with GPU infrastructure and technologies such as PyTorch, CUDA, NCCL, and high-performance networking.\n\nUnderstand concepts such as quotas, priorities, preemption, gang scheduling, topology awareness, and workload admission.\n\nCan diagnose performance and reliability problems across software, orchestration, networking, storage, and hardware.\n\nCare about developer experience and enjoy turning complex infrastructure into simple, reliable interfaces.\n\nThrive in an ambiguous, fast-moving environment shaped by frontier AI research.\n\nWhat We Offer\nWe offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.\nFor the most up-to-date details on benefits available in your location, please refer to our Benefits page.\nPrivacy Policy\nYour privacy matters to us. 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