Post-Doctoral AI Researcher (2 year Fixed-Term)

About the Role

This 24-month fixed-term position is for a Post-Doctoral AI Researcher to join the AI Research team. The role is designed for recent PhD graduates looking to deepen their research experience at the frontier of AI, specifically applying machine learning to high-impact real-world domains like medicine, finance, and law.

Key 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 & Tech Stack

• Proficiency in Python.
• Experience training deep learning models using PyTorch, JAX, TensorFlow, or equivalent frameworks.

Qualifications

• 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).
• 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.

Bonus Points

• 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.