Senior Machine Learning Engineer I, AI & ML Platform

Spring Health•San Francisco, CA

1d•Hybrid

About The Position

Spring Health is hiring a Senior Machine Learning Engineer for its AI & ML Platform team. Reporting to the Senior Engineering Manager of the AI & ML Platform team, this role will help build and scale a centralized platform of services, tools, and best practices that supports AI and ML development and powers the company’s care capabilities. The role will enable teams to deploy generative AI capabilities more rapidly and reliably across the organization. This is a hybrid role based in San Francisco, with an expectation to work from the office at 44 Montgomery two to three days per week. Candidates must be based in the San Francisco area or be able to relocate independently within 90 days of their start date. Occasional travel for team off-sites is required. Spring Health is a global mental health company whose AI-native platform provides personalized support through self-guided tools, coaching, therapy, medication management, and specialty care. The company expects team members to use AI tools thoughtfully, apply human judgment to AI outputs, and continue building AI fluency in support of their roles and the company’s mission.

Requirements

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 generative AI and machine learning libraries and frameworks, such as LangChain, Pydantic, and Scikit-Learn.
Experience maintaining and configuring third-party and internally built engineering tools, including deploying them within a Kubernetes stack.
Experience driving adoption of generalized platforms while continuously improving internal developer experience.
Ability to architect solutions for a full project before implementing a new feature, software, or tool in an enterprise environment, accounting for project and organizational constraints.
Ability to mentor junior engineers and communicate complex technical ideas to technical and non-technical audiences to build buy-in and alignment.

Nice To Haves

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 in cloud environments such as AWS or Azure and in Kubernetes clusters.
1–2 years of Ruby on Rails experience.

Responsibilities

Collaborate to build and scale the AI platform, tooling, and best practices to enable rapid deployment of generative AI capabilities across the organization.
Monitor and maintain the uptime and availability of critical AI and ML tools and platform features.
Help prioritize the backlog by identifying high-impact engineering opportunities that increase adoption of the centralized AI platform.
Participate in on-call rotations, host internal office hours, and contribute to cross-functional AI working groups as a technical advocate for the team.
Lead refactoring initiatives for key platform services and establish and enforce centralized coding standards.
Partner with machine learning teams to consult on and periodically modernize MLOps practices.
Collaborate across teams to accelerate adoption of AI and ML platform offerings, acting as a technical partner and consultant to feature teams.
Troubleshoot and debug AWS, Azure, and Kubernetes infrastructure issues to maintain platform reliability and availability.
Identify process improvements that help balance feature development, operational support, and internal consultations.
Meet internal support SLAs, including a 24-hour initial response and active follow-up or resolution within 72 hours for cross-functional engineering inquiries.
Help reduce the time required to bring generative AI features to production and improve engineering-team NPS by reducing implementation friction and establishing adoption standards.

Benefits

Health, dental, and vision benefits starting on the first day.
One Medical accounts for employees and dependents.
HSA and FSA plans, with Spring contributing up to $1,000 to HSAs depending on plan type.
Employer-sponsored 401(k) match of up to 2%.
Yearly allotment of no-cost visits to Spring Health therapists, coaches, and medication management providers for employees and dependents.
Paid time off, including vacation, sick leave, and company holidays.
Parental leave after six months of employment: 18 weeks for birthing parents and 16 weeks for non-birthing parents.
Access to Noom, a psychology-based weight management program.
Fertility care support through Carrot and up to $4,000 in reimbursement for related fertility expenses.
Access to Wellhub for fitness, mindfulness, nutrition, and sleep options.
Access to BrightHorizons for sponsored child care, back-up care, and elder care.
Up to $1,000 per year in professional development reimbursement.
$200 per year in donation matching.