Synced from Ashby · Aug 21

Clinical Physician

Pear VCSan Francisco Bay AreaPosted Aug 21, 2026
Internal Medicine PhysicianSenior
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Aug 21
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Ashby
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Job description

About Optexity

Optexity is a product-driven research lab building clinical reasoning and computer use models on data no other lab can reach. We deploy directly inside clinics and hospitals — including systems with no APIs, using integration infrastructure we've built and open-sourced — and in return become their preferred partner. That gives us proprietary clinical reasoning trajectories from practicing physicians, and a feedback loop between real patient encounters, our models, and the products built on top of them.

We're a small, fast-moving founding team with multiple published papers in NeurIPS, ICML, CVPR and backgrounds from Apple, Amazon, Microsoft, CMU, and IIT.

We are backed by world-class investors and leaders including Jeff Dean, Neotribe VC, PearVC, Together Fund, and Zapier Fund.

How we work

  • Customer obsession — we start with the customer and work backwards

  • Intellectual honesty — ideas matter more than titles; we communicate directly and assume good intent, even in disagreement

  • Bias for action — we build and learn with customers rather than debate in the abstract

  • Extreme ownership — we own outcomes, not just tasks, and see problems through

Why this role exists

We're building a clinical AI assistant that sits inside real practices — surfacing care gaps, assembling pre-visit prep, flagging risk, and suggesting treatment plans. None of that works without physicians defining what "correct" means.

Most clinical advisor roles are a monthly call where someone reacts to a demo. This one isn't. You'll be in the product loop weekly: deciding what the assistant should flag, what a good summary actually looks like at 7:50am before a 15-minute visit, and grading where the model is wrong and why. Your judgment becomes our ground truth and our evaluation standard.

We're looking for physicians across internal medicine, cardiology, oncology, and other specialties — both to go deep in your own domain and to help us understand where workflows diverge across specialties.

What you'll do

  • Define what the assistant should surface. Which care gaps matter, which risk signals are worth an interruption, and which are noise that erodes trust. Translate clinical shorthand into precise, checkable criteria.

  • Shape the pre-visit summary. What belongs in it, in what order, at what length — and what a physician actually reads versus scrolls past.

  • Specify clinical search. What you'd want to pull from the chart before a visit, how you'd phrase it, and what a complete answer looks like.

  • Judge the AI. Review model-generated risk flags, differentials, and treatment plans against your own reasoning. Score them, explain the gap, and tell us whether a miss is dangerous or merely different.

  • Build ground truth. Help us construct real-case evaluation sets and rubrics that reflect actual standard of care, not textbook answers.

  • Pressure-test the product. Sit in on design decisions and tell us when something wouldn't survive contact with a real clinic day.

  • Contribute to publications. We publish benchmarks and technical reports; clinical co-authorship is available and encouraged.

Ideal candidate

  • MD or DO, board-certified or board-eligible

  • Currently practicing or recently practicing — active clinical context matters more than titles

  • Internal medicine, cardiology, oncology, or an adjacent specialty; primary care and multi-specialty experience especially welcome

  • Day-to-day fluency in an EMR (Epic, eClinicalWorks, Athena, ModMed, or similar) and an honest view of where it fails you

  • Opinionated about clinical workflow, and able to explain why something is right — not just that it is

  • Comfortable with ambiguity, direct feedback, and being asked "how do you actually know that?"

  • Genuinely interested in AI in medicine, including its failure modes

Nice to have

  • Clinical informatics training or CMIO-adjacent experience

  • Experience with quality measures and value-based care programs (HEDIS, MIPS, CPT Category II, HCC coding)

  • Prior work advising or building health tech products

  • Research or publication background

  • Experience training residents or fellows — the skill of articulating clinical reasoning out loud transfers directly

Commitment

  • Part-time: 5–15 hours/week, flexible around clinical schedules. Async-friendly, with a recurring weekly working session.

  • Full-time: for physicians ready to step into a founding clinical role and own clinical direction end to end.

Remote is fine. Occasional in-person time in San Francisco or at partner clinic sites is a plus.

What you get

  • Direct, daily work with the founders — no layers between your input and what ships

  • Real influence over a product that practicing physicians will use, not a pilot that dies in committee

  • Competitive hourly rate for part-time engagements; competitive salary and meaningful equity for full-time

  • Co-authorship on benchmarks and technical reports

  • Visa sponsorship available for full-time roles

View original posting on Ashby

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Based on publicly available information, candidates applying through Ashby for roles at Pear VC can typically expect a structured online application, often followed by an initial screening conversation with a recruiter or talent team member. The process may include multiple stages, such as functional or role-specific interviews, take-home assessments, or case studies depending on the position, and possibly a conversation with hiring managers or team members who would be close collaborators. Ashby-based processes commonly allow candidates to track application status through an online portal and may include automated updates at various stages. Response times vary and can depend on role seniority and hiring volume. Candidates should generally prepare to discuss relevant experience, technical or domain skills, and motivation for the role, while understanding that exact steps and pacing can differ by team and position.

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