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Spring Health
San Francisco, CA (Hybrid)
Source: Spring Health careers · View original posting
From Spring Health's posting. “We” and “our” refer to the employer.
Our mission: e liminating 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 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.
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.
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.
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