Senior Machine Learning Engineer I, AI & ML Platform

Spring Health is a global mental health company on a mission to eliminate every barrier to mental health. We're building a world where getting support is simple, personal, and built around the person, so care can continue through every job, move, health plan, and life stage.

Our AI-native platform helps us deliver personalized support across self-guided tools, coaching, therapy, medication management, and specialty care. With outcomes independently validated by JAMA Network Open and the Validation Institute, Spring Health reaches more than 170 million people worldwide through leading employers, health plans, and partners.

As an AI-native company, we believe technology should expand the reach, quality, and humanity of care. Every Spring Health team member is expected to use AI tools thoughtfully, apply human judgment to AI outputs, and keep building AI fluency in ways that support their role and our mission.

Reporting to the Senior Engineering Manager of the AI & ML Platform team, this Senior Machine Learning Engineer will play a key part in building and scaling a centralized AI platform (services, tools, best practices, and more) that powers our care capabilities. This role is part of the AI & ML Platform team and is instrumental in stewardship of a shared foundation of AI and ML development at Spring Health.

Please note this is a hybrid role based in San Francisco with an expectation to be in the office 2-3 days per week at our 44 Montgomery location. Candidates must be based in the San Francisco area or able to relocate independently within 90 days of their start date. Occasional travel will be required for team on-sites.

• →Collaborate to build and scale our AI platform, tooling, and best practices, enabling the rapid deployment of GenAI capabilities across the organization.
• →Monitor and maintain the uptime of critical AI and ML tools to ensure high availability for key platform features.
• →Contribute to backlog prioritization by identifying high-impact engineering opportunities that drive internal adoption of our centralized AI platform.
• →Act as a technical advocate for the team by participating in on-call rotations, hosting internal office hours, and contributing to cross-functional AI working groups.
• →Lead refactoring initiatives for key platform services to establish and enforce centralized coding standards for engineering contributors.
• →Partner with machine learning teams to consult on and periodically modernize MLOps best practices.
• →Drive cross-team collaboration to accelerate the adoption of AI and ML platform offerings, serving as a technical partner and consultant to feature teams.
• →Troubleshoot and debug cloud (AWS and Azure) and Kubernetes (K8s) infrastructure issues to ensure the reliability and availability of platform services.
• →Identify and recommend process optimizations to help the team effectively balance feature development, operational support, and internal consultations.

• Maintain internal support service level agreements (SLAs) by achieving a 24-hour initial response time and ensuring active follow-up or resolution within 72 hours for cross-functional engineering inquiries.
• Reduce feature time-to-production for GenAI capabilities to meet established internal velocity targets.
• Improve internal Net Promoter Score (NPS) among engineering teams by reducing implementation friction and establishing centralized adoption standards for GenAI features.

• Degree in Computer Science, Data Science, or a related field with a focus on Artificial Intelligence and Machine Learning.
• 4-6 years of Python development experience with GenAI and ML libraries and frameworks (such as LangChain, Pydantic, Scikit-Learn, and more).
• Experience maintaining and configuring engineering tools (third-party and in-house built), including deployment within a Kubernetes stack.
• Experience driving the adoption of generalized platforms with a continuous focus on improving internal Developer Experience (DX).
• Demonstrated ability to architect solutions for a full project as a lead prior to implementing a new feature, software, or tool in an enterprise environment while keeping project and organization constraints in mind.
• Demonstrated ability to mentor junior engineers and communicate complex technical ideas effectively to both technical and non-technical audiences to create buy-in and alignment.

• Experience with MLOps and best practices for creating and deploying machine learning models.
• Background in a DevOps-style operational environment, including on-call rotations and cloud infrastructure management.
• Experience debugging and resolving issues within cloud environments (such as AWS or Azure) and Kubernetes clusters.
• 1-2 years of Ruby on Rails experience

The target base salary range for this position is $183,000 - $205,500, and is part of a competitive total rewards package including stock options and benefits. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all employee pay and compensation programs annually using Radford Global Compensation Database at minimum to ensure competitive and fair pay.