Description
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact.
We are hiring a Post-Doctoral Researcher for our AI Research team. This 24-month fixed-term position is designed for recent PhD graduates looking to deepen their research experience on challenging problems at the frontier of AI , specifically, applying machine learning to high-impact real-world domains like medicine, finance, and law.
As a Post-Doctoral Researcher at Snowflake, you will:
- 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
Our ideal Post-Doctoral Researcher will have:
- 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
Bonus points for the following:
- 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