Post-doctoral Researcher (fixed-term)

Snowflake Snowflake · Data AI · WA-Bellevue, United States · Engineering

Post-Doctoral Researcher in AI Research at Snowflake, focusing on applying machine learning to high-impact real-world domains like medicine, finance, and law. The role involves conducting independent and collaborative research, publishing high-quality work, designing and executing large-scale experiments, and developing models and systems that bridge AI capabilities with real-world application requirements. The position is a 24-month fixed-term contract.

What you'd actually do

  1. 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
  2. Conduct independent and collaborative research and publish high-quality work at top AI and domain-applied research venues
  3. Design and execute large-scale experiments using modern deep learning frameworks, writing high-quality, reusable code
  4. Develop models and systems that bridge AI capabilities with real-world application requirements in high-stakes, regulated domains
  5. Engage across teams — including with domain experts and applied engineering — to ground research in practical constraints and real-world impact

Skills

Required

  • 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 have

  • 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

What the JD emphasized

  • publishing high-quality work
  • high-stakes, regulated domains

Other signals

  • applying machine learning to high-impact real-world domains like medicine, finance, and law
  • publishing high-quality work
  • develop models and systems that bridge AI capabilities with real-world application requirements