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About This Role
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.
The Lead PM, AI Safety and Platformwill 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.
What you’ll do:
- 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.
What success looks like:
- 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.
What you’ll bring:
- 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
Nice to have:
- 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.
Benefits provided by Spring Health:
Note: We have even more benefits than listed here and below, your recruiter will provide more in-depth information as you continue in the interview process. Benefits are subject to individual plan requirements and eligibility criteria.
- Health, Dental, Vision benefits start on your first day at Spring. You and your dependents also receive access to One Medicalaccounts HSA and FSA plans are also available, with Spring contributing up to $1K for HSAs, depending on your plan type.
- Employer sponsored 401(k) match of up to 2% for retirement planning
- A yearly allotment of no cost visits to the Spring Health network of therapists, coaches, and medication management providers for you and your dependents.
- We offer competitive paid time off policies including vacation, sick leave and company holidays.
- At 6 months tenure with Spring, we offer parental leave of 18 weeks for birthing parents and 16 weeks for non-birthing parents.
- Access to Noom, a weight management program—based in psychology, that’s tailored to your unique needs and goals.
- Access to fertility care support through Carrot, in addition to $4,000 reimbursement for related fertility expenses.
- Access toWellhub, which connects employees to the best options for fitness, mindfulness, nutrition, and sleep in one subscription
- Access to BrightHorizons, which provides sponsored child care, back-up care, and elder care
- Up to $1,000 Professional Development Reimbursement a year.
- $200 per year donation matching to support your favorite causes.
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.
Salary Context
This $182K-$242K range is above the median for AI Product Manager roles in our dataset (median: $193K across 99 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Spring Health, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $182K to $242K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Spring Health AI Hiring
Spring Health has 5 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, MLOps Engineer. Positions span Seattle, WA, US, New York, NY, San Francisco, CA, US. Compensation range: $205K - $242K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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