Post-Doctoral Researcher

Responsibilities

• Collaborate with research mentors to formulate research projects or novel applications of machine learning aligned with the team's mission, with a focus on AI applied to medicine, finance, or law
• Conduct independent and collaborative research and publish high-quality work at top AI and domain-applied research venues
• Design and execute large-scale experiments using modern deep learning frameworks, writing high-quality, reusable code
• Develop models and systems that bridge AI capabilities with real-world application requirements in high-stakes, regulated domains
• Engage across teams — including with domain experts and applied engineering — to ground research in practical constraints and real-world impact
• Contribute to the broader research community through publications, open-source releases, and collaboration with academic and industry partners

Requirements

• PhD (or expected completion by start date) in Computer Science, Statistics, Mathematics, or a related STEM field, or equivalent practical experience
• Research experience in machine learning or AI techniques (e.g., open-source projects, campus lab experience, research internships, or publications)
• Proficiency in Python and experience training deep learning models using PyTorch, JAX, TensorFlow, or equivalent frameworks
• One or more scientific publication submissions in top AI or applied domain research venues (e.g., NeurIPS, ICML, ICLR, AAAI, ACL — or equivalent high-quality venues in medicine, finance, or law)
• Ability to work independently and collaborate effectively across research teams and domain experts

Nice-to-Haves

• Research background at the intersection of AI and one or more of: medicine (clinical NLP, medical imaging, drug discovery), finance (quantitative modeling, risk, forecasting, market analysis), or law (legal NLP, contract analysis, reasoning, compliance)
• Experience designing, fine-tuning, or evaluating LLMs or foundation models in applied domain settings
• Familiarity with evaluation challenges in high-stakes AI — fairness, explainability, robustness, or regulatory constraints
• Publications at domain-applied AI venues (e.g., CHIL, ML4H, FinNLP, NLLP)
• Experience with retrieval-augmented generation (RAG), knowledge graphs, or structured reasoning over domain-specific data