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 physiological 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 ML engineer, applied researcher, or software engineer with a focus on ML systems
- Strong coding skills in Python with a track record of writing clean, production-quality code
- Experience designing, deploying and operating ML inference systems at scale (real-time streaming and/or large-scale batch)
- 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: 2+ years of experience applying advanced mathematical and statistical techniques
- Preferred: Experience working with time series data (wearable, physiological, or high-frequency sensor data)
