Senior Machine Learning Engineer I, AI & ML Platform
Spring Health66 · San Francisco, CA (Hybrid)
About this role
**Senior Machine Learning Engineer I, AI & ML Platform** **About Spring Health** Spring Health is a global mental health company on a mission to eliminate every barrier to mental health. Their AI-native platform helps deliver personalized support across self-guided tools, coaching, therapy, medication management, and specialty care—reaching more than 170 million people worldwide. **Role Overview** Reporting to the Senior Engineering Manager of the AI & ML Platform team, you’ll help build and scale a centralized AI platform (services, tools, best practices, and more) that powers Spring Health’s care capabilities. This role is also key to stewarding a shared foundation for AI/ML development. **Location / Schedule** Hybrid role based in **San Francisco** (44 Montgomery). Expect **2–3 days/week in the office**. Candidates must be based in the SF area or able to relocate independently within **90 days**. Occasional travel for team on-sites. --- ## What you’ll do - Collaborate to build and scale the AI platform, tooling, and best practices to enable rapid deployment of GenAI capabilities. - Monitor and maintain uptime of critical AI/ML tools for high availability. - Identify high-impact engineering opportunities to drive internal adoption of the centralized AI platform. - Serve as a technical advocate via on-call rotations, internal office hours, and cross-functional AI working groups. - Lead refactoring initiatives for key platform services to establish and enforce centralized coding standards. - Partner with ML teams to consult on and modernize MLOps best practices. - Drive cross-team collaboration to accelerate adoption of AI/ML platform offerings. - Troubleshoot and debug cloud (AWS/Azure) and Kubernetes (K8s) infrastructure issues. - Recommend process optimizations to balance feature development, operational support, and internal consultations. ## What success looks like - Maintain internal support SLAs: **24-hour initial response** and **72-hour follow-up/resolution** for cross-functional inquiries. - Reduce time-to-production for GenAI features to meet internal velocity targets. - Improve internal engineering NPS by reducing implementation friction and establishing centralized adoption standards. ## What you’ll bring - Degree in Computer Science, Data Science, or related field focused on AI/ML. - **4–6 years** of Python development with GenAI and ML libraries/frameworks (e.g., LangChain, Pydantic, Scikit-Learn, etc.). - Experience maintaining/configuring engineering tools (third-party and in-house), including deployment within Kubernetes. - Experience driving adoption of generalized platforms with a focus on improving Developer Experience (DX). - Ability to architect solutions end-to-end as a lead while accounting for enterprise constraints. - Ability to mentor junior engineers and communicate complex technical ideas to technical and non-technical audiences. ### Nice to have - MLOps experience and best practices for creating/deploying ML models. - DevOps-style operational experience (on-call, cloud infrastructure management). - Debugging/resolving issues in AWS/Azure and Kubernetes clusters. - **1–2 years** of Ruby on Rails experience. --- ## Compensation Target base salary range: **$183,000 – $205,500** (plus competitive total rewards including stock options and benefits). Individual pay varies by factors including experience and location. ## Benefits (highlights) - Health, Dental, Vision starting on day one; access to **On
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