Senior Machine Learning Engineer, Health

Responsibilities

• Create, improve, and maintain production services that provide analysis for health features in collaboration with Data Scientists and MLOps Engineers.
• Collaborate with Data Engineers to improve ML data pipelines, tooling, and validation systems that support robust model performance.
• Work alongside data scientists to translate research prototypes into production ML systems optimized for scale, latency, and cost efficiency.
• Collaborate with researchers and product teams to align model development with health insights and member impact.
• Participate in on-call rotations for data science services, ensuring uptime and performance in production environments.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or a related field (Master’s preferred).
• 4+ years of professional experience as a Machine Learning Engineer or Software Engineer building production ML-enabled systems.
• Proven experience working with time series data (wearable/physiological/high-frequency sensor data preferred).
• Experience designing, deploying, and operating ML inference systems at scale (real-time streaming and/or large-scale batch).
• Strong coding skills in Python with a track record of writing clean, well-tested, production-quality code.
• Strong fundamentals in backend/service development (APIs, reliability, monitoring, debugging) as it relates to serving ML models.
• Experience deploying and maintaining ML systems on cloud platforms (AWS or GCP), including CI/CD and observability practices.
• Familiarity with applied ML development (frameworks, evaluation criteria, performance validation) and translating prototypes into production systems.

Preferred

• Experience developing ML-enabled software in a regulated or quality-managed environment (e.g., QMS-controlled development for SaMD/medical devices), including documentation, traceability, validation/verification practices, and change control.
• Demonstrated technical leadership through architecture/design ownership, setting engineering standards, and raising quality via reviews and mentorship.
• Proven track record driving measurable improvements in system performance, reliability, and/or cost at scale, and influencing cross-functional technical direction.