Staff Machine Learning Engineer (Health)

RESPONSIBILITIES:

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Design, build, and maintain production services that deliver health features, in close collaboration with Applied ML Scientists and ML Research Engineers.

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Collaborate with Data Platform teams to improve ML data pipelines, tooling, and validation systems that support robust model performance.

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Work alongside Applied ML Scientists to translate research prototypes into production ML systems optimized for scale, latency, and cost efficiency.

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Partner with the Digital Health team on algorithmic performance specifications, validation and verification planning, and the design of SPA or algorithm validation studies.

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Collaborate with researchers and product teams to align model development with health insights and member impact.

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Participate in on-call rotations for data science services, ensuring uptime and performance in production environments.

QUALIFICATIONS:

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Bachelor's degree in Computer Science, Data Science, Applied Mathematics, or a related field (Master's preferred). 7+ years of professional experience as a Machine Learning Engineer or Software Engineer building production ML systems.

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Proven experience working with time series data (wearable, physiological, or high-frequency sensor data preferred).

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Experience designing, deploying, and operating ML inference systems at scale (real-time streaming and/or large-scale batch).

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Strong coding skills in Python with a track record of writing clean, well-tested, production-quality code.

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Strong fundamentals in backend/service development (APIs, reliability, monitoring, debugging) as it relates to serving ML models.

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Experience deploying and maintaining ML systems on cloud platforms (AWS or GCP), including CI/CD and observability practices.

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Familiarity with applied ML development (frameworks, evaluation criteria, performance validation) and translating prototypes into production systems.

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Experience developing ML-enabled software in a regulated or quality-managed environment (SaMD or medical device), with working knowledge of change control, quality documentation, traceability, and verification/validation practices.

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Demonstrated technical leadership through architecture and design ownership, setting engineering standards, and raising quality through reviews and mentorship.

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Proven track record driving measurable improvements in system performance, reliability, and/or cost at scale, and influencing cross-functional technical direction.