Product Lead, Abridge

• 5 to 8 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
• Strong communication skills and the ability to translate complex technical concepts into clear decisions and narratives
• A track record of delivering high quality products in domains where accuracy, reliability, and trust are paramount

Desirable

• 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 owns a critical slice of how we deliver on that promise
• The Note Quality group sits at the center of this mission. It owns the models that power documentation, the evaluation systems that measure quality at scale, and the specialty and workflow engines that ensure our output matches how clinicians practice across diverse settings
• We are hiring a Product Manager to drive product strategy and execution within Note Generation. You will partner closely with engineering, ML, design, clinical leaders, and our specialty councils. You will help set the bar for note quality, contribute to our model and evaluation roadmap, and ship the table stakes and differentiated capabilities required to win the market
• Drive AI product strategy and execution within Note Generation. Shape the roadmap for a core area of Abridge's note models and evaluation stack, and own outcomes against it
• Contribute to the evaluation platform and measurement systems. Help build a world class note quality evaluation system across human annotation, LLM judges, automated backtesting, and in production analytics. Partner with ML and engineering on reliable benchmarks, continuous validation, and fast iteration cycles
• Drive depth across specialties and clinical contexts. Own specialty priorities within your area and ensure specialty notes reflect clinical expectations, use the right terminology and structure, and match professional norms across care settings. Partner with clinical leaders to define gold standards
• Cross functional execution. Work closely with engineering and ML to align on architecture, model iteration schedules, evaluation methodologies, and system reliability. Partner with product design to define user control surfaces that leverage model outputs while keeping clinicians in control. Collaborate with customer facing teams to translate real world feedback into the model development loop
• Operate with a high bar for quality, speed, and accountability