{"id":1709520,"url":"https://alion.io/job/boston-dynamics-staff-ml-ops-engineer","title":"Staff ML Ops Engineer","company":{"id":47994,"name":"Boston Dynamics","domain":"bostondynamics.com","url":"https://alion.io/company/boston-dynamics","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-08T05:49:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Waltham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":180000,"currency":"USD","period":"year","gross":null,"usd_annual":180000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Ansible","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"MLFlow","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scrum","optional":false},{"name":"SLURM","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Weights & Biases","optional":false},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-02T16:43:17Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-09T03:57:28Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"As an MLOps Engineer on the Central Software (CSW) ML Platform team, you will play an active role in implementing the tools, infrastructure, and pipelines that unify how our product and research teams get stuff done. This is your chance to work closely with ML/RL engineers and researchers from across Boston Dynamics (BD), supporting advanced AI product and research activities at the forefront of robotics innovation.\nHow you will make an impact\nTransform proofs of concept into scalable solutions, helping product teams deliver new robot capabilities to customers\nEvolve and scale fielded solutions, enabling continuous model improvement and redeployment\nWork with stakeholders across BD to understand requirements, ensuring deployed solutions meet end-user needs\nOwn end-to-end delivery of new capabilities, spanning implementation, testing, deployment, and operations\nMaintain our GPU clusters and develop automation to monitor and improve cluster health\nUse observability tools to monitor system health and root-cause problems across the platform\nDrive accuracy and efficiency by profiling and optimizing ML data, training, and evaluation pipelines.\nWork closely with other members of the ML Platform team to implement, deploy, and maintain ML infrastructure\nBe an active participant in our agile development process, coordinating work with others, calling out challenges, and regularly communicating progress\nUse your experience to mentor and upskill peers and other contributors across the organization\nYou bring\n5+ years of experience as a Senior Software Engineer or ML Engineer\nProficiency in Python and related ML frameworks (PyTorch, TensorFlow, Pandas, NumPy)\nExperience with cloud platforms (e.g., GCP, AWS) and scalable ML deployment methods (Docker, Kubernetes, Ansible, Terraform)\nExperience with GPU cluster management and scheduling (e.g., Slurm, Kueue, or similar)\nExperience with CI/CD practices applied to ML pipelines\nExperience with experiment tracking and model/data versioning tools (e.g., MLflow, Weights & Biases, DVC)\nExperience building scalable data and ETL pipelines (e.g., Spark, Airflow) alongside data processing, augmentation, and cleaning techniques.\nExperience with Agile, Scrum, or other lean methodologies; ability to work collaboratively in cross-functional teams\nBachelor's degree in Engineering, Computer Science, or a related technical field, or equivalent practical experience\nNice to have\nExperience with networking fundamentals such as IAP, Tailscale, Shared VPC, NAT\nFamiliarity with database optimization concepts such as indexing and connection pooling.\nExperience with TypeScript, Node, and related full-stack web technologies to build internal tooling, visualization dashboards, and web-based interfaces for MLOps platforms\nExperience with on-robot/edge deployment constraints (latency, compute limits, OTA model updates\nExperience with annotation tools such as SAM or Co-Tracker\nWe are interested in all qualified candidates eligible to work in the United States. However, we are not able to sponsor visas for this position.\nThe base pay range for this position is between $150,000 to $180,000 annually. Base pay will depend on multiple individualized factors including, but not limited to internal equity, job related knowledge, skills and experience. This range represents a good faith estimate of compensation at the time of posting. Boston Dynamics offers a generous Benefits package including medical, dental vision, 401(k), paid time off and an annual bonus structure. 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