Product Lead, Foundational Models and Post-Training
Remote • SF Office • FullTime
Posted 18d ago
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Abridge is seeking a Product Lead to drive the strategy for its foundational models and post-training efforts. This role sits at the intersection of model science, platform strategy, and clinical product delivery, focusing on leveraging Abridge's unique corpus of clinical conversations and clinician feedback to enhance model capabilities. You will own the product strategy, connecting research initiatives to tangible product outcomes, and determining where in-house models offer a competitive advantage. The ideal candidate will have a deep understanding of the modern model-development lifecycle and the technical judgment to collaborate effectively with scientists and engineers, while always anchoring the work in clinician value, patient safety, and business impact.
Responsibilities
- Set the product strategy for Abridge's model family, translating priorities into a coherent portfolio across capability, quality, latency, cost, safety, and controllability.
- Own the path from research to product impact, defining hypotheses, milestones, decision gates, and success metrics for model deployment.
- Shape how proprietary data is used as training and feedback signals in partnership with relevant teams.
- Build frameworks for model investment decisions, quantifying expected quality, cost, latency, control, and strategic benefits.
- Define product-relevant capabilities and failure modes for model use cases in partnership with the Evals team.
- Create a tight learning loop with product teams to convert production failures and feedback into training priorities.
- Drive cross-functional execution, aligning various teams around priorities and delivery.
- Communicate complex research choices clearly to executives and product teams.
Requirements
- 7+ years of product management or closely related experience, with substantial ownership of ML-powered products, model platforms, or AI infrastructure.
- Proven track record of turning ambiguous technical capabilities into shipped products with measurable outcomes.
- Strong working knowledge of the modern model-development lifecycle (data strategy, fine-tuning, evaluation, inference, experimentation, production monitoring).
- Technical judgment to reason with ML scientists and engineers about training objectives, data quality, model selection, scaling, latency, and serving cost.
- Strong product judgment on when proprietary models offer differentiation versus when external models or conventional systems are better.
- Experience creating clarity across multiple teams, defining decision rights, sequencing dependencies, and resolving disagreements.
- High bar for evidence, safety, and trust, understanding that aggregate scores can hide consequential failures.
- Excellent written and verbal communication skills, including explaining technical strategy to diverse audiences.