Remote | Founding Role | Part-Time → Scalable
We are building a healthcare-focused human data company designed to support frontier AI labs in improving model reasoning, safety, and evaluation performance.
This is not an annotation startup.
We are looking for a founding technical lead who has already worked inside the frontier AI ecosystem and understands how model training pipelines actually consume human data.
Requirements:
You must have direct experience working with a frontier AI lab (e.g. OpenAI, Anthropic, Google DeepMind, Meta AI, etc.) on:
* RLHF or RLAIF
* Model evaluation
* Red teaming
* Human data pipelines
* Alignment or reasoning improvement
What You Will Own:
* Design of high-signal healthcare datasets targeting real model failure modes
* Construction of structured correction artifacts (not generic Q&A)
* Development of scoring rubrics and evaluation frameworks
* Creation of the initial validation dataset used to secure commercial contracts
* Definition of training standards for clinician contributors
* Technical credibility with frontier labs
You will be setting the bar for what “high-quality healthcare human data” means.
The Profile We’re Looking For:
* Consultant-level Doctor (or equivalent senior clinical experience)
* First-hand exposure to how frontier models ingest, filter, and score human data
* Strong intuition around model blind spots (hallucination, overconfidence, shallow reasoning, calibration errors)
* Able to think like both a clinician and a model evaluator
* High agency, startup mentality
Compensation Structure:
* Competitive consulting rate
* Performance-based upside
* Potential equity pathway based on validated impact and ARR milestones
Why This Role Is Different:
Most healthcare AI data companies compete on volume.
We compete on signal.
If you understand the difference - and have operated at that level - this will be obvious to you.