Director of Product Management

Role Overview

Join Abridge as Director of Product Management (Note Generation). You will own the product vision, strategy, and execution, lead a team of product managers, and partner closely with engineering, ML, design, clinical leaders, and specialty councils.

Key Missions

• Lead product vision, strategy, and execution for Note Generation, managing PMs and coordinating with cross-functional partners.
• Set and raise the bar for note quality by guiding the model and evaluation roadmap and ensuring market-winning capabilities ship.
• Drive clinical and specialty depth, owning the specialty roadmap and prioritizing specialties that drive adoption and revenue.

Responsibilities / What Success Looks Like

• Define and execute roadmap decisions that balance long-term architectural investments with near-term quality improvements.
• Own evaluation of model quality, including evaluation frameworks, annotation pipelines, and benchmark design.
• Work on systems that improve model performance in production, including evaluation platforms and ML quality/observability.

Requirements

• 7 to 10+ years of product management experience with significant ownership of ML-powered products or platform systems.
• Experience partnering closely with ML researchers and engineers to deliver production impact.
• Deep understanding of measuring and improving model quality (evaluation frameworks, annotation pipelines, benchmark design).
• Strong technical fluency across ML, data pipelines, and distributed systems.
• Ability to translate complex technical concepts into clear decisions and narratives.
• Proven track record delivering high-quality products where accuracy, reliability, and trust are paramount.
• Experience managing PMs and leading high-performing product teams.

Nice-to-Haves

• Experience building evaluation platforms, ML observability, or quality measurement pipelines.
• Background in clinical/healthcare/regulated environments requiring accuracy and compliance.
• Experience with personalization systems, context ingestion frameworks, or ambient intelligence.
• Experience overseeing specialty- or domain-specific model adaptations.
• Experience shipping large-scale ML products with human-in-the-loop workflows.