Senior Machine Learning Engineer II, AI & ML Platform

Our mission: eliminating every barrier to mental health.

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 II will own the design and scaling of 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 setting the technical direction for a shared foundation of AI and ML development at Spring Health.

Location: 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.

What you'll do

• Lead the technical direction for key areas of our centralized AI platform (services, tooling, and best practices), breaking complex, ambiguous problems into straightforward solutions that let teams ship GenAI capabilities quickly across the organization.

• Own the reliability, scalability, and cost-effectiveness of critical AI and ML platform services, designing systems that hold up when components fail.

• Partner with product and engineering stakeholders to identify the highest-impact problems blocking platform adoption, and turn them into implementation plans for the current and next quarter.

• Lead hypercare investigations and high-stakes incident responses across cloud (AWS, Azure) and Kubernetes infrastructure, communicating progress clearly to stakeholders.

• Set the standard for engineering excellence on the platform by leading refactoring initiatives that align contributors on simple, coherent designs.

• Serve as a technical advisor to ML and feature teams across service domains: modernize MLOps practices, lead cross-functional AI working groups, and coordinate cross-team efforts to speed up platform adoption.

• Give high-value feedback in RFCs and design reviews, and help reduce risk on other teams’ projects.

• Lead projects that streamline how the team balances feature development, operational support, and internal consultations.

• Mentor engineers on technical skills and career growth, and set stretch goals that push the team to think bigger.

What success looks like in this role

• Maintain internal support SLAs across the team: a 24-hour initial response and active follow-up or resolution within 72 hours for cross-functional engineering inquiries.

• Reduce feature time-to-production for GenAI capabilities so that Feature Lead Time for new GenAI and ML features is under internally set target for engineers across Spring Health.

• Improve internal NPS among engineering teams by reducing implementation friction and setting centralized adoption standards for GenAI and ML features.

• Improve the reliability and cost-efficiency of critical Generative AI and ML platform services, measured by adoption metrics, time to first token, and other SLIs defined for the team.

• Incidents in production environments for the team’s services are led to resolution, with a root-cause analysis and follow-up actions completed within 1 week.

What you'll bring

• Degree in Computer Science, Data Science, or a related field with a focus on AI and Machine Learning, or equivalent practical experience.

• 6–8+ years of professional software development experience, including deep Python experience with GenAI and ML libraries and frameworks (such as LangChain, Pydantic, Scikit-Learn, and more).

• Proven experience designing, deploying, and maintaining engineering tools and platforms (third-party and in-house built), including within a Kubernetes stack.

• Track record of driving adoption of generalized platforms and continuously improving internal Developer Experience (DX).

• Demonstrated ability to architect and lead complex, multi-team solutions in an enterprise environment, with comprehensive design documentation before implementation.

• Experience leading multi-person, multi-sprint technical projects and aligning stakeholders across teams.

• Use AI to reason about system-level tradeoffs and integrate AI into team workflows, establishing quality standards and critically reviewing all AI-generated output.

• Demonstrated ability to mentor engineers and communicate complex technical ideas to both technical and non-technical audiences.

Nice to have:

• 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

Not sure if you meet every requirement? Research shows that women and people from historically underrepresented communities often hesitate to apply for roles unless they meet every qualification compared to other similarly-qualified candidates. At Spring Health, we are committed to fostering a workplace where everyone feels valued, empowered, and supported to Thrive. If this role excites you, we encourage you to apply.

Our privacy policy: https://springhealth.com/privacy-policy/

Spring Health is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex, marital status, ancestry, disability, genetic information, veteran status, gender identity or expression, sexual orientation, pregnancy, or other applicable legally protected characteristic. We also consider qualified applicants regardless of criminal histories, consistent with applicable legal requirements. Spring Health is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans. If you have a disability or special need that requires accommodation, please let us know.