Product Lead, Abridge

• 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

Desirable

• 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
• 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