Staff ML Platform Engineer

Role overview Staff ML Platform Engineer role on a team that builds the paved road for moving machine learning models from notebook to production in a regulated health-data environment. The position sets technical direction across training, serving, and observability, owning the pipelines and infrastructure that allow data science teams to ship and run models safely against sensitive clinical data.

Responsibilities - Set technical direction across ML training, serving, and observability, and serve as the final escalation point for the hardest infrastructure problems (GPU capacity, distributed compute tuning, production incidents). - Own and evolve the paved-road framework, including shared CI/CD, model-workflow scaffolding, and bundle-based assets, so data science teams can ship workflows from a config file without bespoke plumbing. - Lead architecture for large language model endpoint serving across managed providers and self-hosted deployments, covering latency, cost, caching, evaluation, and safe routing of sensitive prompts. - Set the standards and tooling for experiment tracking, model registries, training image supply chains, and observability across training and inference. - Partner with adjacent platform, application, and operations teams to present a coherent ML platform experience. - Provide key technical input into vendor and tooling selection for model providers, ML tooling, and observability. - Mentor senior engineers, guide platform consumers, and stay hands-on writing high-leverage code and infrastructure as code.

Requirements - 10+ years of software engineering experience, with at least 3 years designing, evolving, and operating enterprise-scale ML platforms in production. - Strong technical judgment under ambiguity, with a track record of setting standards, influencing peers, and raising the engineering bar across teams. - Hands-on production experience with at least one major managed ML platform (Databricks and/or Amazon SageMaker), an experiment tracking and registry system, and at least one core ML framework such as PyTorch or TensorFlow. - Proficiency in Python and a JVM language (Java or equivalent), with real depth in Apache Spark for large-scale distributed compute. - Deep familiarity with AWS networking, identity, GPU compute, storage, and messaging, and the judgment to choose the right primitive for the job. - Fluency with Terraform, containers, Kubernetes, and GitHub-based CI/CD for ML workloads. - Direct production experience serving large language models, including cost management, evaluation harnesses, and safe handling of sensitive prompts and outputs. - Daily, native use of AI coding assistants such as Claude Code, Cursor, or Copilot, with opinions on how they make a team faster and how to apply them responsibly around sensitive data. - Clear written and verbal communication, especially in remote, cross-functional settings.

Nice to have - Experience operating in regulated environments handling protected health information or similarly sensitive data. - Background defining paved-road or self-service platforms consumed by other engineering and data science teams.

Benefits and work setup - Estimated total cash compensation range of $224,000–$280,000 USD, with actual offer determined by level, location, experience, and skills. - Eligibility for health screenings and vaccinations may be required depending on client site policies, with accommodations considered case by case. - Role is not eligible for employment sponsorship.