Join Counsel Health as a Physician AI Engineer to build AI-enabled care delivery systems in a fully remote environment.
Posted by employer 1 month ago
First seen on Joblaze 20 hours ago
Last verified on the company career page 20 hours ago
What you'll build
Must have
Nice to have
AI in the day-to-day
Building an AI-enabled care delivery platform with clinical reasoning translated into shippable agents.
Requirements
Not disclosed in this posting: compensation, visa sponsorship.
Joblaze summary
The Physician AI Engineer at Counsel Health plays a crucial role in developing AI-driven clinical tools by integrating clinical insights with technical execution. This position requires a strong background in medicine, particularly with hands-on experience in building large language models, as well as a proven track record of delivering products in health tech environments. Ideal candidates are those who thrive in early-stage startups and possess the ability to navigate the complexities of both clinical reasoning and engineering challenges.
Joblaze insights
Quick facts
From the original posting
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Counsel Health is building an AI-enabled care delivery platform, and we need someone who can sit at the intersection of clinical, technical, and product judgment to own its 0→1 build. This is a builder role for someone who can translate real clinical reasoning directly into shippable agents, safety systems, and evaluations that meet a practicing physician's bar for patient care.
The components of clinical AI most likely to harm or help patients, the diagnostic agents, the safety nets, the evals that gate them, can't be built well by engineers alone, physicians alone, or PMs alone. We're looking for one person who is genuinely all three: someone who can scope a clinical problem, design the eval that proves whether it's solved, and ship the code themselves.
Own the full arc of clinical and safety agents: scoping with Product and relevant teams, writing the PRD, designing the architecture and evals, building, and shipping to production.
Stand up a clinical eval framework that becomes the canonical quality bar for at least one agent, that’s built to be testable and productionized so any engineer or researcher can run their work against it and know whether it's safe to ship.
Measurably improve core components of Counsel AI (i.e. history-taking, differential diagnosis, triage, treatment planning), benchmarked against physician performance on representative case sets.
Get fluent in the existing stack quickly by reading the code, running the evals, sitting with the clinical team, and surfacing a prioritized list of where Counsel’s AI and safety systems are weakest.
Ship early, guided work end-to-end from shadowing current workflows, picking a tractable agent or safety component, prototyping it, getting it through review, and shipping it.
MD, DO, or international equivalent, with residency training or 3+ years of clinical experience completed.
Substantive, hands-on experience building with LLMs in production, beyond just experimentation.
A track record of real ownership over shipped products in industry or startup settings; health tech experience is strongly preferred.
Board-eligible or board-certified status with meaningful active or recent clinical practice (strongly preferred).
Published research, open-source work, or public writing on clinical AI, LLM evaluations, or related areas (strongly preferred).
Experience embedded inside an engineering organization (strongly preferred).
You think like a physician and build like an engineer, meaning you are equally comfortable reasoning through a differential diagnosis and shipping products that operationalizes it.
You've operated at an early-stage startup, or founded one yourself, and you're energized rather than overwhelmed by ambiguity and a fast-moving roadmap.
You have an experimental mindset: you'd rather prototype, test, and iterate than wait for the perfect plan.
You take patient safety personally and are motivated by getting medical AI right, not just getting it out the door.
You're a natural translator across disciplines such that you can make clinical nuance legible to engineers and technical tradeoffs legible to clinicians.
You're drawn to defining your own roadmap rather than executing someone else's spec.
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