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Spring Health is a global mental health platform on a mission to eliminate barriers to mental health. The company operates an AI-native platform delivering personalized support through self-guided tools, coaching, therapy, medication management, and specialty care, reaching over 170 million people worldwide through employers, health plans, and partners.
Reporting to the Senior Engineering Manager of the AI & ML Platform team, this Senior Machine Learning Engineer will be instrumental in building and scaling a centralized AI platform that powers Spring Health's care capabilities. The role focuses on stewardship of shared AI and ML development foundations across the organization.
Key responsibilities include collaborating to build and scale the AI platform, tooling, and best practices to enable rapid GenAI deployment; monitoring and maintaining uptime of critical AI/ML tools; contributing to backlog prioritization by identifying high-impact engineering opportunities; acting as a technical advocate through on-call rotations, office hours, and cross-functional working groups; leading refactoring initiatives to establish centralized coding standards; partnering with ML teams on MLOps modernization; driving cross-team adoption of platform offerings; troubleshooting cloud (AWS/Azure) and Kubernetes infrastructure issues; and recommending process optimizations.
Success metrics include maintaining 24-hour SLA response times for cross-functional inquiries, reducing feature time-to-production for GenAI capabilities, and improving internal NPS among engineering teams by reducing implementation friction.
Required qualifications: degree in Computer Science, Data Science, or related field with AI/ML focus; 4-6 years Python development with GenAI and ML libraries (LangChain, Pydantic, Scikit-Learn); experience maintaining and configuring engineering tools within Kubernetes; demonstrated ability to drive platform adoption with focus on developer experience; ability to architect solutions as a lead in enterprise environments; and demonstrated mentoring and technical communication skills.
Nice-to-have skills include MLOps experience, DevOps/on-call background, cloud infrastructure debugging, and 1-2 years Ruby on Rails experience.
The role is hybrid based in San Francisco (44 Montgomery location) with expectation of 2-3 days per week in office. Candidates must be based in the San Francisco area or able to relocate within 90 days.
