{"id":2643875,"url":"https://alion.io/job/true-talent-solutions-partners-machine-learning-infrastructure-engineer","title":"Machine Learning Infrastructure Engineer","company":{"id":4415660,"name":"True Talent Solutions Partners","domain":"truetalentsolutionspartners.com","url":"https://alion.io/company/true-talent-solutions-partners","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":400000,"currency":"USD","period":"year","gross":null,"usd_annual":400000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Docker","optional":false},{"name":"FSDP","optional":false},{"name":"GCP","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Physical AI","optional":false}],"status":"live","first_seen_at":"2026-10-15T17:55:10Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-15T23:08:57Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nJoin an AI for science team building large-scale models for physical systems. You will develop the infrastructure behind distributed training and inference, helping the team advance models that can predict outcomes and inform effective actions.\nWhat You'll Do\nDesign, deploy, and maintain distributed machine learning training and inference clusters.\n\nBuild scalable end-to-end pipelines for petabyte-scale datasets and model training across the machine learning lifecycle.\n\nEvaluate parallelization methods and numerical precision trade-offs at different model scales.\n\nProfile and debug low-level GPU operations to improve performance.\n\nTrack relevant research and apply promising ideas to infrastructure and model workflows.\n\nWhat We're Looking For\n2 to 10 years of experience building large-scale machine learning infrastructure for pretrained or foundation models, including compute, data, inference, and distributed training systems.\n\nExperience optimizing large-scale training, data, or inference workloads, including low-level GPU performance work with CUDA and JAX.\n\nProficiency with distributed training frameworks such as FSDP or DeepSpeed.\n\nExperience with cloud platforms such as GCP, AWS, or Azure, and with containers and orchestration tools such as Docker and Kubernetes.\n\nFamiliarity with scalable model serving, distributed task management, monitoring, logging, observability, and version control practices.\n\nBackground in physical AI or AI for science, such as robotics, autonomous systems, or biology, and an interest in working across the machine learning lifecycle.\n\nComfort working in a fast-paced environment and learning unfamiliar domains.\n\nCompensation & Benefits\nBase salary range: $200,000 to $400,000 annually. 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