[Hiring] Member of Technical Staff, Benchmarking @GenMD

Role Description

Frontier models still fail on real clinical work, and most of those failures never show up in public benchmarks. You'll build the benchmarks and evaluation systems that show where models break on real-world healthcare data, then turn those findings into the data that closes the gap. We call the loop measure, build, re-test.

What you'll do:

• Design and maintain clinical benchmarks for reasoning, longitudinal patient histories, multimodal records and tool use.
• Build evaluation pipelines end to end: runs, scoring, dashboards and reports that compare models and training runs at scale.
• Create rubrics, automated graders and LLM-as-a-judge systems, and calibrate them against clinician review.
• Run systematic failure analysis: categorize failure modes, measure how often they occur and feed the results into dataset design and post-training.
• Work with clinical experts and partner AI teams to turn benchmark results into research papers and products.

Qualifications

• Hands-on experience evaluating or training LLMs (PyTorch, Hugging Face or similar).
• Strong Python and data engineering skills; comfortable with large tabular and text datasets (SQL/BigQuery a plus).
• Research taste: you can design an evaluation that measures what matters and resists shortcuts.
• B.Sc. or higher in CS, ML, NLP, biomedical informatics or a related field; LLM/NLP publications preferred.
• About 4 hours of daily overlap with Pacific Time (roughly 9 AM to 1 PM PT).

Nice to have

• Experience with clinical data (EHR, claims, FHIR/OMOP, clinical notes) or medical benchmarks.
• Experience with RL from verifiable rewards, reward modeling or post-training data.

Compensation

$150,000 – $210,000 per year, plus equity. Final offer depends on experience and location.