Senior Clinical Specialist, AI Systems

Function Health•Austin, TX

About The Position

Function Health is seeking a deeply experienced clinician to join our Clinical Product team as a Senior Clinical Specialist, AI Systems. You will work with our product, engineering, and clinical teams to ensure the outputs of our AI systems that reach our members are clinically correct, clinically useful, and safe. The Clinical Product team is embedded in product development from kickoff. We own the clinical quality bar for surfaces like the Action Plan, the Clinician Note, Health Journeys, and member-facing AI chat. What this role contributes above all is clinical judgment applied to complex clinical scenarios and the ability to translate your findings to diverse cross-functional audiences. You will be contributing both upstream and downstream of our AI efforts. Upstream, you will be involved in supporting the design of monitoring and evaluation systems for our AI products. Downstream, you will be evaluating clinical outputs that incorporate highly diverse data points like blood biomarkers, wearables, omics, imaging, and gut microbiome. You will be inspecting what our AI systems produced to spot inaccuracies, omissions, and any harmful content. While hands-on experience in clinical AI is highly advantageous but not mandatory, a desire to learn how AI systems work is essential. The ideal candidate is comfortable working through ambiguity, meticulous in their attention to detail, self-motivated, resourceful, and capable of thriving in a fast-paced environment. As an early member of this team, your contributions will play a critical role in shaping the future of our member experience as we scale from hundreds of thousands of members to millions. This role is best suited for those energized by fast-paced environments, excited to build in a high-growth setting, and deeply motivated by our mission at Function. If you're looking for meaningful challenges, dynamic work, and the opportunity to make a real impact, we’d love to meet you.

Requirements

MD, DO, or equivalent clinical doctorate, with substantial hands-on clinical practice.
You have carried real clinical responsibility for real patients.
You have deep expertise in one or more clinical domains, ideally with an understanding of systems medicine approaches.
You are someone who goes beyond symptoms and diagnostic codes, always striving to identify root causes of clinical imbalances in your patients and understand the multi-dimensional nature of health.
You can apply strong clinical reasoning under timeline pressure and over long, dense tasks.
You can separate what is established, what is plausible, and what is speculative, and educate the team appropriately on each.
You are genuinely curious about how AI systems work and have a demonstrated ability to pick up technical craft quickly.
Prior experience with LLMs, evaluation, or machine learning is not required — the willingness to learn it here, and the rigor to hold it to a clinical standard, is.
You have exceptional communication skills.
You have the ability to adapt clinical information for very different audiences — engineers, PMs, designers, and members.
A track record of owning something end-to-end in a cross-functional setting and driving it to completion.
You are comfortable with ambiguous and complex tasks, can operate autonomously under uncertainty on 0-to-1 projects, but know when to ask for support from the team.

Nice To Haves

You have experience interpreting complex blood biomarkers, advanced imaging, genomics, gut microbiome, and wearable data.
Any prior exposure to clinical AI products, LLM evaluation, or prompt and context engineering — a strong plus, but explicitly not a requirement; what matters is your curiosity and willingness to learn.
Background in longevity, functional, or root-cause medicine.
Prior early-stage or high-growth startup experience.

Responsibilities

Serve as the embedded clinical partner to a product pod, liaising between clinical, product, engineering, and licensed clinicians, and bringing systems-based, root-cause medicine principles into the build.
Establish deep partnership with Engineering to design and run clinical AI evaluations.
Define what “clinically meaningful” output looks like, set quality thresholds, build rubrics, and judge accuracy, clinical intent, omission, hallucination, and medication-safety failure modes against real data.
Help translate clinical reasoning into technical specification — prompts, context and decision logic — partnering with engineers to identify where clinical logic must be embedded in AI systems rather than described around them.
Own the clinical quality bar end-to-end for one or more AI product areas — from clinical requirements at kickoff through launch validation and ongoing monitoring.
Flag when product behavior must evolve as evidence, guidelines, or the clinical standard of care shift.