Abridge's ability to deliver the most accurate, personalized, and clinically useful notes is the foundation of our product strategy. Note quality determines clinician trust and our right to expand into more workflows. This role will be responsible for the quality and level of personalization around all notes.
Requirements
- 7 to 10+ years of product management experience with significant ownership of ML powered products or platform systems.
- Deep understanding of how to measure and improve model quality, including evaluation frameworks, annotation pipelines, and benchmark design.
- Strong technical fluency across ML, data pipelines, and distributed systems.
- Experience working closely with ML researchers and engineers to drive impact in production.
- Ability to balance long term architectural investments with near term quality improvements.
- You have experience building evaluation platforms, ML observability systems, or quality measurement pipelines.
- You have worked on personalization systems, context ingestion frameworks, or ambient intelligence products.
Responsibilities
- Lead AI product strategy for Note Generation.
- Define the long term strategy for Abridge’s core note models and evaluation stack.
- Oversee the evaluation platform and measurement systems.
- Build a world class note quality evaluation system across human annotation, LLM judges, automated backtesting, and in production analytics.
- Partner with ML and engineering to ensure reliable benchmarks, continuous validation, and fast iteration cycles.
- Own the specialty roadmap and ensure we prioritize specialties that drive adoption, revenue opportunities, or deeper defensibility.
- Ensure that specialty notes reflect clinical expectations, use the right terminology and structure, and match professional norms across care settings.
Other
- Lead and develop a team of PMs.
- Foster a culture of excellence, speed, and accountability.
- Cross functional leadership.
- Partner with Product Design to define user control surfaces that leverage model outputs while keeping clinicians in control.
- Collaborate with customer facing teams to understand quality expectations and close the loop between real world feedback and model development.
