Product Lead, Foundational Models and Post-Training

Abridge•San Francisco, CA

12d•$250,000 - $290,000•Onsite

About The Position

Abridge was founded to power deeper understanding in healthcare. Our ambient AI platform transforms clinical conversations into high-quality documentation and is expanding into clinical workflows such as coding, clinical decision support, and care navigation. The quality of these products depends on the models beneath them - and on our ability to improve those models using signals that only Abridge can access at scale. We are hiring a Product Lead to partner with ML Science on Abridge's foundation-model and post-training work. You will help turn our proprietary corpus of clinical conversations, clinician edits, EHR context, and downstream actions into durable model capabilities. You will own the product strategy that connects research bets to product outcomes: where an in-house model can create meaningful advantage, which capabilities and workloads to prioritize, what evidence is required to scale an approach, and how a successful model moves from experiment to production. This is not a traditional feature-PM role, and it is not a research-program-manager role. You will operate at the intersection of model science, platform strategy, and clinical product delivery. You will need enough technical depth to challenge assumptions and make consequential tradeoffs with scientists and engineers, while keeping the work anchored in clinician value, patient safety, and business impact. You must be located in San Francisco for this opportunity or willing to relocate to San Francisco.

Requirements

7+ years of product-management or closely related experience, including substantial ownership of ML-powered products, model platforms, or AI infrastructure.
A track record of turning ambiguous technical capabilities into shipped products and measurable user or business outcomes.
Strong working knowledge of the modern model-development lifecycle, including data strategy, fine-tuning and preference optimization, evaluation, inference, experimentation, and production monitoring.
The technical judgment to reason with ML scientists and engineers about training objectives, reward design, data quality, model selection, scaling, latency, and serving cost - without pretending to be the scientist in the room.
Strong product judgment about where proprietary models create durable differentiation versus where external models or conventional systems are the better choice.
Experience creating clarity across multiple teams: defining decision rights, sequencing dependencies, resolving disagreement, and maintaining speed in a high-ambiguity environment.
A high bar for evidence, safety, and trust. You know that aggregate model scores can hide consequential failures and that clinical AI requires explicit escalation, abstention, and human-review paths.
Excellent written and verbal communication, including the ability to explain a technical strategy to both research teams and company leadership.

Nice To Haves

You have directly worked on LLM post-training, reinforcement learning, preference optimization, distillation, model routing, or domain-adaptive pretraining.
You have managed a portfolio spanning frontier APIs, open-source models, and in-house models.
You have experience with human-feedback or expert-annotation systems, especially where feedback is sparse, subjective, or expensive.
You have shipped AI in healthcare, life sciences, or another safety-critical and regulated domain.
You have worked on products involving clinical documentation, medical reasoning, agentic workflows, or longitudinal context.
You have partnered with research teams on high-compute bets where staged evidence was required before scaling investment.

Responsibilities

Set the product strategy for Abridge's model family. Translate company and product priorities into a coherent portfolio of models, with explicit choices across capability, quality, latency, cost, safety, and controllability.
Own the path from research to product impact. Define the hypotheses, milestones, decision gates, and success metrics that move models from experiments into shadow mode and production.
Turn proprietary data into a product advantage. Shape how Abridge uses de-identified conversations, final notes, clinician edits, EHR context, and care actions as training and feedback signals, in partnership with Data, Privacy, Security, and Clinical teams.
Make model investment decisions legible. Build clear frameworks for when to use a frontier model, an open model, a prompted workflow, or an Abridge-trained model; quantify expected quality, serving-cost, latency, control, and strategic benefits.
Partner with Evals on promotion criteria. Define the product-relevant capabilities and failure modes that matter for each model use case, while the Evals team owns shared measurement infrastructure and neutral evaluation standards.
Create a tight learning loop with product teams. Convert production failures, clinician feedback, edit behavior, and emerging product needs into training priorities; make model improvements visible in user and business outcomes.
Drive cross-functional execution. Align ML Science, ML Engineering, Product Engineering, Clinical Science, Data, Evals, and product pods around priorities, interfaces, ownership, and delivery - often without formal authority.
Communicate the strategy. Make complex research choices understandable to executives and product teams, clearly separating demonstrated results, working hypotheses, and long-term bets.

Benefits

Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees
Comprehensive Health Plans: Medical, Dental, and Vision coverage for all full-time employees and their families.
Generous HSA Contribution: If you choose a High Deductible Health Plan, Abridge makes monthly contributions to your HSA.
Paid Parental Leave: Generous paid parental leave for all full-time employees.
Family Forming Benefits: Resources and financial support to help you build your family.
401(k) Matching: Contribution matching to help invest in your future.
Personal Device Allowance: Tax free funds for personal device usage.
Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits.
Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more.
Mental Health Support: Dedicated access to therapy and coaching to help you reach your goals.
Sabbatical Leave: Paid Sabbatical Leave after 5 years of employment.
Compensation and Equity: Competitive compensation and equity grants for full time employees.