# Senior Machine Learning Engineer I, AI & ML Platform at Spring Health

- Company: Spring Health
- What the company does: Eliminating barriers to mental healthcare. Clinically-proven technology with world-class providers to deliver precisely what your employees need.
- Company website: https://www.springhealth.com
- Type: Startups (AI role)
- Level: Senior
- Location: San Francisco, CA (Hybrid)
- Work setup: Hybrid
- Pay: $183K to $206K base salary per year (USD)
- Posted: 2026-08-13
- Apply by: 2026-10-08
- Apply: https://job-boards.greenhouse.io/springhealth66/jobs/4723968005
- Page: https://www.1752.vc/careers/jobs/spring-health-senior-machine-learning-engineer-i-ai-and-ml-platform/

## About the role

The target base salary range for this position is $183,000 - $205,500 , and is part of a competitive total rewards package including equity 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.

## What they're looking for

- 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

Tags: Technology