Description
What You’ll Be Doing
Design and build large-scale, real-time ML systems with a deep understanding of platform fundamentals, including systems that must meet strict latency requirements, such as sub-second response budgets for patient-provider matching
- Implement core ML platform components with the same rigor and code quality expected of a senior backend software engineer, including algorithmic and systems-level work
- Own critical ML infrastructure components end to end: Feature Stores, Online/Offline Parity, Deployment Safety, Monitoring, Failure Modes, and Feedback Loops
- Define the technical vision and 1–3 year roadmap for the ML platform
- Partner closely with Data Science, Product and Engineering teams, translating business goals like match rate and provider utilization into clear platform requirements, and maintaining strong communication across the modeling/platform boundary
- Drive adoption of an AI-first development mindset, reaching for AI tooling where appropriate and ensuring the platform can efficiently serve both static and live models at scale
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