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.
• 5+ years of professional experience as a Machine Learning Engineer or Software Engineer with focus on ML systems.
• Proven expertise working with time series data (wearable, physiological, or high-frequency sensor data strongly preferred).
• Experience designing and deploying ML inference systems at scale: both real-time streaming and large-scale batch pipelines.
• Strong coding skills in Python (scientific stack) and SQL, with a track record of writing clean, production-quality code.
• Strong communication skills to collaborate across engineering, research, and product teams.
• Proven experience deploying and maintaining ML systems on cloud platforms (AWS or GCP)
• Working familiarity with MLOps best practices: model versioning, CI/CD for ML, observability, and monitoring for inference systems.
• Ability to reason about and design for performance trade-offs (latency vs. throughput vs. cost) when building ML inference systems.
• Strong understanding of backend service development (APIs and service reliability) as it applies to serving ML models at scale.