{"id":4044042,"url":"https://alion.io/job/true-talent-solutions-partners-machine-learning-infrastructure-engineer-5","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":true,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C++","optional":false},{"name":"CUDA","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":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-11-10T18:03:08Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-10T23:13:04Z","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 team building large-scale models for physical systems, with weather as an initial application. You will develop the distributed infrastructure needed to train and serve foundation models, helping advance AI that can predict outcomes and identify 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 manage workloads across the machine learning lifecycle.\n\nResearch and evaluate training approaches, including parallelization methods and numerical precision trade-offs at different model scales.\n\nProfile and debug low-level GPU operations to improve performance.\n\nFollow relevant research and apply promising ideas to infrastructure and model workflows.\n\nWhat We're Looking For\nTwo to ten years of experience building large-scale machine learning infrastructure for pretrained or foundation models.\n\nHands-on experience with compute clusters, data infrastructure, distributed training, and inference systems.\n\nExpertise optimizing large-scale training, data, or inference workloads, including low-level GPU performance analysis.\n\nExperience with distributed training frameworks such as FSDP or DeepSpeed; familiarity with CUDA, JAX, Python, C++, Linux, or NVIDIA hardware is valuable.\n\nKnowledge of cloud platforms such as GCP, AWS, or Azure, and containerization and orchestration tools such as Docker and Kubernetes.\n\nBackground in physical AI or AI for science, such as robotics, autonomous systems, or biology, and comfort working across the machine learning lifecycle.\n\nFamiliarity with scalable task management and model serving, observability practices, and experiment analysis such as ablation studies.\n\nA track record of working effectively in fast-paced environments and on time-sensitive projects.\n\nCompensation & Benefits\nBase salary: $200,000 to $400,000 annually. Visa sponsorship is not available.\nLocation\nOn-site in San Francisco, California. 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