Member of Clinical Staff, Life Sciences

The Role

Abridge's clinical credibility is built on ensuring our AI outputs are clinically meaningful, contextually appropriate, and trustworthy to physicians across specialties and workflows. The Clinician Scientist team sits at the heart of this mission, serving as the essential bridge between cutting-edge AI and the complex realities of medical practice.

We are hiring a Clinician Scientist to focus on a new frontier: the research and life sciences applications of the clinical data Abridge generates. Ambient capture produces a uniquely rich, longitudinal record of what actually happens in the exam room — signals that are largely invisible in claims and structured EHR data. This role owns the clinical science behind turning that into research-grade evidence: trial feasibility and cohort identification, real-world evidence study design, endpoint definition, and the quality standards that make life sciences partners trust the output.

You will be embedded with the Life Sciences pod and work in close partnership with ML Science, Product Management, Design, Legal/Privacy, and our external research and pharma partners.

Must be willing to work from our SF or NYC 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 or New York City.

What You'll Do

• Own clinical credibility for Life Sciences products. Serve as the clinical authority on trial feasibility, cohort definition, real-world evidence, and research-facing outputs. Ensure what we produce would hold up to scrutiny from a principal investigator, a regulator, or a pharma medical affairs team.

• Define endpoints and outcome measures. Determine what can be credibly measured from ambient data — symptom trajectory, functional status, adherence, treatment response — and where the data cannot support a claim.

• Build scalable clinical evaluation systems. Partner with ML Science to develop LLM judges, annotation frameworks, and evaluation pipelines for Life Sciences extraction tasks. Ensure evaluation methodologies reflect clinical and epidemiologic judgment.

• Bridge clinical and technical domains. Serve as a translator between clinical and research reality and ML/engineering capabilities. Help engineers and ML scientists understand clinical nuance, confounding, and bias; help clinicians and partners understand technical constraints and opportunities.

• Partner with life sciences and research organizations. Act as a credible clinical counterpart in conversations with pharma, CRO, and academic partners. Scope studies, shape feasibility assessments, and surface what is achievable and valuable.

• Uphold rigor, privacy, and regulatory standards. Partner with Legal, Privacy, and Compliance on de-identification, consent, IRB pathways, and the evidentiary standards expected in regulated research and healthcare settings.

• Close the feedback loop. Bring insights from partners and real-world usage back into product direction, and represent the clinical and research perspective in roadmap planning and prioritization.

What You Bring

• Active clinical background. MD, DO, or equivalent clinical degree with 3+ years of clinical practice experience. Board certification preferred.

• Research and quantitative training. MD/PhD, MD/MPH, or MD with a strong data science background preferred.

• Life sciences or clinical trials experience. Experience in a data science, medical affairs, clinical development, or research role at a pharma or biotech company, or in a consulting role serving pharma.

• Technical fluency with AI/ML. Working understanding of NLP, LLMs, prompt engineering, and model evaluation. You do not need to be an ML researcher, but you should be able to speak credibly with ML scientists about evaluation methodology and quality measurement.

• Systems thinking. Ability to design scalable processes and frameworks rather than providing one-off clinical input.

• Strong communication and influence. Comfortable presenting to technical, clinical, and external partner audiences. Skilled at earning trust across engineers, ML scientists, product managers, and outside researchers.

• Pragmatic problem-solver. Bias toward action and iteration. Comfortable with ambiguity and able to make progress even when clinical or methodological "gold standards" don't exist.

Bonus Points If…

• You have worked with real-world data assets — claims, registries, EHR-derived datasets (e.g., Flatiron, Optum), or trial data — and know their strengths and failure modes firsthand.

• You have completed a fellowship or advanced training in clinical informatics, pharmacoepidemiology, health services research, or biomedical informatics.

• You have published peer-reviewed work in clinical trials, pharmacoepidemiology, or AI for healthcare.

• You can code (Python, SQL) and are comfortable working with data, running analyses, or prototyping evaluation scripts.

• You have worked at a health tech company, EHR vendor, or AI healthcare startup in a product or applied research role.

How we take care of Abridgers:

• Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees

• ... and much more!