Clinician Scientist

About Abridge

Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.

Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.

We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh.

The Role

Abridge is looking for Clinician Scientists to drive the development of our AI-powered clinician tools. We’re looking for someone who can combine deep clinical expertise with a background in AI. In this role, you will help shape AI-driven tools and ensure our models generate accurate, high-quality products that enhance clinician efficiency and patient care. You’ll work closely with engineers, researchers, product managers, and clinicians to refine AI models, validate outputs, and develop new capabilities that improve documentation workflows.

Must be willing to work from our SF office at least 3x per week

This position requires a commitment to a hybrid work model, with the expectation of coming into the office a minimum of three times per week. Relocation assistance is available for candidates willing to move to San Francisco.

What You’ll Do

• Develop and refine AI-driven clinical tools across notes, risk adjustment (HCC capture), clinical decision support, and prior authorization using clinical expertise and prompt engineering
• Define what “clinically meaningful” output looks like for each product area, including acceptable error rates, failure modes, and quality thresholds
• Collaborate with cross-functional teams (engineers, data scientists, clinicians) to integrate medical insights into AI models.
• Write, review, and optimize prompts to improve AI model performance in clinical contexts.
• Build and refine evaluation tools to streamline medical documentation quality assessment, including hallucination detection, omission detection, and medication safety checks
• Translate clinical needs into technical specifications and data models.
• Define clinical guardrail metrics and baselines; own pre/post evaluation requirements for any model update that affects clinical output
• Contribute to product development, revenue cycle management, and broader business initiatives
• Monitor clinical guideline update cycles and flag when product behavior needs to evolve based on new standards or payer policies

What You’ll Bring

• MD, PA, DO (or equivalent clinical degree) with direct experience working in clinical practice
• Deep understanding of medical documentation, clinical workflows, and medical terminology
• Ability to evaluate AI-generated clinical notes, provide structured feedback, and guide improvements
• Strong knowledge of healthcare data standards (e.g., FHIR, HL7) and privacy regulations (e.g., HIPAA)
• Hands-on experience in clinical data validation and quality assessment
• Experience collaborating with engineers and product teams to build AI-powered clinical tools
• Strong analytical and problem-solving skills
• Excellent written and verbal communication skills

Nice to Have

• Experience in software engineering, particularly in prompt engineering or AI model development
• Python programming skills and experience with deep learning frameworks such as PyTorch, JAX, and/or TensorFlow
• Previous experience in AI-powered clinical documentation tools or clinical decision support systems
• Published research in AI, machine learning, or healthcare technology