Product Lead, Foundational Models and Post-Training

About Abridge

Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.

Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.

We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh.

The Role

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.

What You'll Do

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.

What You Bring

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.