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

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 look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.

Snowflake is about empowering enterprises to achieve their full potential, and people too. With a culture that's all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology and careers to the next level.

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. You'll collaborate closely with Snowflake researchers, scientists, and engineers on fundamental and applied problems, publishing high-quality work while helping shape how AI integrates with the world's most consequential industries.

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