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Abridge

Builds AI that writes notes from doctor visits

Member of Product Staff - Foundation Models & Post-Training

Technical Product ManagementUpdated on Nov 13
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • At least 7 years of product-management or closely related experience, including substantial ownership of machine-learning-powered products, model platforms, or artificial intelligence 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.
  • Technical judgment to reason with machine-learning scientists and engineers about training objectives, reward design, data quality, model selection, scaling, latency, and serving cost.
  • 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 by defining decision rights, sequencing dependencies, resolving disagreement, and maintaining speed in a high-ambiguity environment.
  • A high bar for evidence, safety, and trust, including awareness that aggregate model scores can hide consequential failures and that clinical artificial intelligence requires explicit escalation, abstention, and human-review paths.

Responsibilities

  • Set product strategy for Abridge's model family by translating 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 by defining hypotheses, milestones, decision gates, and success metrics that move models from experiments into shadow mode and production.
  • Shape how Abridge uses de-identified conversations, final notes, clinician edits, electronic health record context, and care actions as training and feedback signals, in partnership with Data, Privacy, Security, and Clinical teams.
  • 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 by defining product-relevant capabilities and failure modes for each model use case, while the Evals team owns shared measurement infrastructure and neutral evaluation standards.
  • Create a learning loop with product teams by converting production failures, clinician feedback, edit behavior, and emerging product needs into training priorities, and make model improvements visible in user and business outcomes.
  • Drive cross-functional execution by aligning 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 and make complex research choices understandable to executives and product teams, clearly separating demonstrated results, working hypotheses, and long-term bets.

Desired Qualifications

  • Direct experience with large language model post-training, reinforcement learning, preference optimization, distillation, model routing, or domain-adaptive pretraining.
  • Experience managing a portfolio spanning frontier application programming interfaces, open-source models, and in-house models.
  • Experience with human-feedback or expert-annotation systems, especially where feedback is sparse, subjective, or expensive.
  • Experience shipping artificial intelligence in healthcare, life sciences, or another safety-critical and regulated domain.
  • Experience working on products involving clinical documentation, medical reasoning, agentic workflows, or longitudinal context.
  • Experience partnering with research teams on high-compute bets where staged evidence was required before scaling investment.
  • Excellent written and verbal communication, including the ability to explain a technical strategy to research teams and company leadership.

About the Company

Abridge

Builds AI that writes notes from doctor visits

Abridge builds AI software that listens to conversations between clinicians and patients and drafts the clinical notes doctors would otherwise type, mainly for large health systems. In 2024, Kaiser Permanente rolled it out across its hospitals and medical offices in what was then the largest generative AI deployment in health care. With the patient's consent, clinicians record the visit, and a structured note is ready in their electronic health record when it ends, with each part traceable to the transcript and audio. It also captures orders, billing codes, and patient summaries, and health systems pay for it through enterprise subscriptions. Abridge was founded in Pittsburgh in 2018 and is based in San Francisco.

Company size
201-500
Company stage
Series E
Total funding
$757.5M
Headquarters
San Francisco, CA
Founded
2018

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