Lead Product Manager, AI Safety and 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.

The Lead PM, AI Safety and Platform will drive the product roadmap and business impact of two teams in Spring’s R&D organization: AI Trust and Safety and AI/ML Platform. These teams build capabilities and infrastructure that enable many other teams at Spring to ship AI features quickly and safely. In this role, you will also drive the development of Spring's external AI safety benchmark, VERA-MH, and help establish Spring as a recognized leader in AI safety within mental health care. This is a full-time, that reports to the VP Product, Customer Value and Platform.

Please note that this is a hybrid role based in one of our Hub Cities - New York, San Francisco, or Seattle with an expectation to be in the office 2–3 days per week. Candidates must be based in the NYC, SF or Seattle metro areas or able to relocate independently within 90 days of their start date. Occasional travel will be required for team on-sites. 

• →Own the roadmap and strategy across both the AI/ML Platform and AI Trust & Safety pods, advancing a unified vision for infrastructure that enables rapid, scalable AI development and a standard for what "safe and effective AI" means at Spring Health.
• →Run continuous discovery across product, clinical, engineering, legal, and data science teams to surface emerging AI use cases, workflow gaps, and safety risks; translate those insights into a single prioritized roadmap balancing near-term enablement against long-term platform scalability and trust.
• →Prioritize investments based on business impact, while defining and evangelizing standards across two dimensions: platform performance and safety performance.
• →Measure and report the impact of platform infrastructure investments, tracking how they unlock velocity, quality, and value for downstream feature teams, and using that data to guide where the platform invests next.
• →Drive a strategy to responsibly leverage Spring's unique clinical data assets to continuously improve the performance and safety of our AI systems.
• →Partner with ML engineers and data scientists to scope technical work spanning both platform capabilities and safety/evaluation tooling, with clear problem statements, success metrics, and definitions of done.
• →Define operational approval gates and rollout plans for AI features before production deployment, in close partnership with engineering and clinical leadership.

• Strong evaluation protocols for LLMs and AI-driven features, with measurable improvement in the safety and reliability of AI interactions.
• Reduced infrastructure burden and friction for feature teams, with expanded self-serve access to high-value data and platform capabilities - enabling teams to ship AI features both faster and more safely.
• Operational alignment across stakeholders (legal, clinical, ML) on what constitutes trustworthy AI
• Clear audit trail and incident response plan for AI-related regressions or safety issues
• Core infrastructure in place that Spring's AI Transformation efforts can reliably build on, reducing friction for AI native operations internally.

• 7+ years of product management experience, with a focus on ML/AI systems
• 2+ years of experience shaping the tooling or technical strategy behind GenAI systems, including agent orchestration (LangChain, LangGraph), model gateways, and evaluation pipelines
• Demonstrated experience partnering deeply with Engineering to ship scalable, reliable systems in production environments.
• Experience running continuous discovery with internal users and translating feedback into high-impact platform improvements.
• Strong AI & ML fluency. Understands model lifecycles, performance metrics, failure modes, and validation methods.
• Experience defining goals and specs for model evaluation, red teaming, or risk mitigation
• Exceptional cross-functional communication and alignment skills across technical, clinical, and regulatory teams

• Experience in the benefits or mental health spaceExperience with B2B2C customersTool or language specific experience [that is not required but would be beneficial]

The target base salary range for this position is $182,000 - $242,000, 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.