*hiring on behalf of our client
Title: Research Scientist / AI Engineer (Clinical Logic)
Time: Full-Time
Location: Remote (US or Canada)
Compensation: $150K - $300K
The Company
Our client is a clinical AI and formalization company. They are building a future where the standard of care is executable: where what medicine knows is what medicine does, at every visit, in every setting. The accumulated, evidence-graded knowledge of medicine still lives in documents built to be read, not executed. They compile that knowledge into decision artifacts that are measurable against the standard, deterministic where they must be, and traceable to their source evidence. Their compiler is live in clinical production today: clinicians in public health networks act on its recommendations every day.
Role Overview
This is a research role with production stakes focused on studying how far frontier models can be pushed in extracting, structuring, and proving clinical logic. The ideal candidate will design the experiments that settle what works and ship the winning approach into the compiler pipeline. This role is suited for someone with publication experience in clinical AI who wants to move beyond research prototypes into production systems where clinical decision logic is fully inspectable.
Key Responsibilities
• Build the pipeline by converting clinical standards into executable modules, including parsing with document and layout models, transformation, and validation.
• Design the target syntax and iterate on the representation for guideline logic.
• Run experiments using open versus closed models, fine-tuning, and constrained decoding.
• Co-design benchmarks, datasets, metrics, and error analysis for clinical fidelity.
• Work directly with founders and clinicians to validate outputs, exclusions, precedence, and recommendations.
• Build benchmarks and adversarial cases that measure source fidelity, logical completeness, and recommendation correctness.
• Turn model failures into hypotheses, experiments, and improved system design to enhance the production compiler.
• Develop deterministic validators using static analysis, constraint solving, or formal methods to make ambiguity, contradiction, and source silence explicit.
Qualifications
• 3+ years of research or industry experience, or a final-year PhD or postdoc.
• BS or higher in Computer Science, Machine Learning, Mathematics, or a related technical field.
• Experience working in a Frontier AI Lab.
• Demonstrated track record of shipping ML/NLP systems to production.
• Work authorization for the US or Canada (not open to visa sponsorship, with potential exceptions for employees of frontier labs).
• Production-level proficiency with Python, PyTorch, and the Transformers ecosystem.
• Hands-on experience with LLM evaluation, training, or prompt engineering.
• Ability to articulate failed experiments and uncertainty clearly in writing.
• Willingness to attend in-person offsites multiple times per year.