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About This Role
Senior Product Manager (Artificial Intelligence)
New York City
Product
Hybrid
Full\-time
About Fabric Health
At Fabric Health, we are powering boundless care by solving healthcare’s biggest challenge: clinical capacity. We aren’t here to disrupt healthcare; we’re here to fix it. We unify the care journey from intake to treatment, using intelligent automation to remove administrative burdens and make care delivery 2\-10x more efficient. Our technology empowers clinicians to move faster and focus on what matters most: the patient.
We are a mission\-driven team of brilliant minds trusted by leading organizations including Intermountain Health, OSF HealthCare, SSM Health, and MUSC Health. Our vision is backed by premier investors such as Thrive Capital, GV (Google Ventures), General Catalyst, and Salesforce Ventures. We move quickly for good reason, listen deeply to solve big challenges, and build products with the same care and quality we’d want for our own loved ones. Learn more: About Us \| News \& Press \| LinkedIn \| Careers
About the Role
We are looking for a Senior Product Manager, AI to own the AI\-powered experiences that sit at the heart of how clinicians deliver care and how members navigate it. You will lead product strategy across both surfaces, the tools clinicians use every day and the experiences patients rely on, and translate the capability of modern AI into outcomes that actually matter in healthcare.
We have shipped our first AI features into production. This role takes those bets from working to winning, sets the strategy for what we build next, and defines the quality bar for what it means to ship AI safely in a regulated environment. If you have shipped LLM or ML\-powered products to real users and you want to do it where the work compounds for clinicians and patients, you will thrive here.
What You'll Do
As the Senior Product Manager, AI, you will own the AI product strategy and execution for Fabric across both clinician\-facing and member\-facing surfaces. Your primary responsibilities will include:
- Own the product strategy and roadmap for AI experiences across clinician\-facing and member\-facing surfaces, with clear bets, sequencing, and success criteria.
- Partner with engineering, applied ML, design, and clinical leadership to ship AI products that move the metrics that matter: clinician time recovered, member activation, care outcomes, cost to serve.
- Scale the AI features already in production. Measure impact rigorously, iterate on both the model and the user experience, and expand to new use cases as evidence comes in.
- Define and operationalize the quality bar for AI in a clinical context. This includes evals, safety thresholds, hallucination guardrails, and the operational playbook for shipping responsibly.
- Make build\-versus\-buy decisions on models, vendors, and infrastructure. Own the tradeoffs between latency, cost, accuracy, and risk, and make them transparently.
- Set the operating cadence for AI product work: how experiments run, how we measure, how we ship safely, and how we communicate AI risk and reward to clinical and executive stakeholders.
- Translate between technical ML capability and clinical workflow reality. Neither side wants to learn the other side's language, and your job is to bridge it.
Why You Might Be a Good Fit* You have shipped LLM or ML\-powered products to real production users, ideally on a clinician\-facing or consumer\-facing surface, and you can talk about what worked and what did not.
- You think in evals, not in vibes. You know how to define a quality bar for an AI feature and instrument against it.
- You are fluent enough in the underlying ML stack that engineers respect you, you can call BS on vendor claims, and you can hold a real conversation about model selection, RAG, fine\-tuning, latency, and cost.
- You believe AI in healthcare has to be safer than AI elsewhere, and you have operational ideas about how to make that real, not just rhetorical.
- You move fast in ambiguous environments. You would rather ship a thoughtful 70% solution this quarter than wait for the perfect 95% one next year.
- You enjoy partnering with clinicians and engineers in equal measure, and you make both groups better at their jobs by working with you.
This Might Not Be The Right Fit If...* You have not actually shipped an AI feature to production users. You have consulted, you have strategized, but you have not owned a launch.
- You are looking for a fully scoped roadmap handed to you. This role requires defining the strategy, not just executing on one.
- You are uncomfortable with the level of risk that comes with shipping clinical\-facing AI. We move carefully, but we do move.
- You prefer building exclusively for one type of user. This role owns both clinician and member surfaces, and you need to be energized by that range.
Your Qualifications* 6\+ years of product management experience, with at least 2 years owning AI or ML\-powered products in production.
- A track record of shipping LLM, ML, or AI\-powered features to real users at meaningful scale.
- Strong working knowledge of LLM fundamentals: evals, prompting, RAG, fine\-tuning tradeoffs, and the latency\-cost\-accuracy curves that shape every real\-world deployment.
- Proven partnership with applied ML, AI infrastructure, or engineering teams. You speak the language and the engineers know it.
- Comfort operating in regulated environments where safety, accuracy, and audit\-readiness are not optional.
- Excellent product judgment and the ability to make calls in ambiguity without waiting for permission.
Bonus Points* Healthcare or clinical product experience, particularly on surfaces used by licensed providers.
- Experience shipping consumer or patient\-facing AI in regulated contexts.
- Background in machine learning, applied science, or engineering before moving into product.
- Familiarity with HIPAA, BAAs, and the operational realities of clinical software.
- Experience leading AI product work across both B2B (clinician) and B2C (member) surfaces.
*The national pay range for this role is $140,000\.00 – $165,000\.00 per year. Actual compensation will be determined by factors such as the candidate's geographic market, experience, skills, and qualifications. Certain roles may also be eligible for additional compensation, including a comprehensive benefits package such as medical, dental, vision, unlimited PTO, and a 401(k) plan, stock options and bonuses. If your compensation requirement is greater than our posted range, please still consider applying; a determination can be made based on unique qualifications. Expected compensation ranges for this role may change over time.At Fabric, we believe that a diverse workforce is essential to our success. We are an equal opportunity employer and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, or any other legally protected characteristic. We actively encourage individuals from all backgrounds to apply.* Recruitment Fraud Alert: Protect Yourself**
Fabric Health is aware of scammers attempting to impersonate employers. To ensure that any recruiting contact you receive is legitimate, please adhere to the following:* Verify the Domain: Official recruitment emails will only come from addresses ending in *@fabrichealth.com* or *@gem.com*. No other domain names are legitimate.
- Official Interview Tools: We use Gem for our recruitment process and Google Meet for all video interviews. Google Meet is always the platform used for your first interview; you will never be sent a Zoom link to set up or conduct an initial interview. All interviews are conducted via video unless specifically stated by our team as an audio call. We never conduct interviews via chat, social media, Skype, or WhatsApp.
- Zoom Usage: Zoom is utilized only for specific meetings set directly by our team for purposes outside of the standard interview process (e.g., coordination or onboarding discussions). It is never the first link you will receive from us.
- Authorized Contact \& Texting: Fabric will only contact you if you have submitted an application or if you are connected to a current employee who shared your information with us. We will only send text messages if you have provided explicit authorization and consent, either through your application or while communicating directly with our team. If you have not explicitly authorized us to reach out, treat any SMS or unsolicited outreach as fraudulent and do not respond.
- Sensitive Data: We will never ask you for sensitive personal or financial documents (ID, banking info, SSN) during the application, interview, or candidacy stages. All sensitive data is handled through secure internal systems post\-offer.
- Verify the Team: You can reference LinkedIn to verify members of our recruiting team; however, please remain vigilant as scammers may create fraudulent profiles. Always cross\-reference the sender's email domain with our official @fabrichealth.com address.
If you question the validity of a contact or receive a suspicious message, do not click any links. Report the issue immediately to careers\[email protected]. Please note: The security inbox is for reporting fraudulent activity only. Do not email this address for application status updates or to share application materials, as these will not be reviewed. Applications are only accepted and reviewed if submitted through our official application portal, and no application status information will be provided via the security email.
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Req ID: PRD2602
Salary Context
This $140K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Fabric Health, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($152K) sits 16% below the category median. Disclosed range: $140K to $165K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Fabric Health AI Hiring
Fabric Health has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Remote, US. Compensation range: $165K - $210K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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