Research Scientist

• Implement experiments across both model training and system harness to advance a state-of-art AI co-clinician.
• Drive research on clinical reasoning and longitudinal disease management, reliable agentic action-taking and automation, and verifiable risk management with continual adaptation.
• Define milestones and build simulation-to-real evaluation frameworks measuring clinical capability, safety, and efficiency.
• Validate hypotheses in-silico and in-vivo with precise clinical evaluations, partner with domain experts for real-world prospective validation.
• Collaborate closely with fellow researchers and help build and scale the long-term research team.

Minimum qualifications:

• PhD in Computer Science, Artificial Intelligence, or a related quantitative field, or equivalent practical experience.
• 4 years of experience in LLM fine-tuning/Reinforcement Learning, autonomous and human-on-loop agent development, and designing evaluation frameworks for real-world applications.
• Experience providing technical leadership to execute research from concept to product.
• Experience with clinical and healthcare workflows, data, and environments.
• Experience partnering with vendors for data and rubric curation in real-world domains, and generating synthetic data and rubrics.

Preferred qualifications:

• Experience building agentic benchmarks involving reasoning-driven API read/write, computer control, or healthcare record integrations
• Experience with training agents for long-horizon tasks, computer control, domain safety, uncertainty calibration, and human-on-the-loop systems.
• Experience in full-lifecycle LLM development, deploying autonomous/human-on-loop agents in safety-critical real-world environments.
• Track record of publishing in AI venues (e.g., NeurIPS, ICML, ICLR), or scientific/medical journals (e.g., Nature).

• PhD in Computer Science, Artificial Intelligence, or a related quantitative field, or equivalent practical experience.
• 4 years of experience in LLM fine-tuning/Reinforcement Learning, autonomous and human-on-loop agent development, and designing evaluation frameworks for real-world applications.
• Experience providing technical leadership to execute research from concept to product.
• Experience with clinical and healthcare workflows, data, and environments.
• Experience partnering with vendors for data and rubric curation in real-world domains, and generating synthetic data and rubrics.