Experience with latent dynamics modeling, model-based RL, or physics-informed neural networks (GraphCast, FourCastNet, AlphaFold-style architectures)
Contributions to open-source ML frameworks or foundation model training codebases
Background in scientific/structured models (molecular modeling, materials science, weather/climate)
Experience building controllable video generation or neural simulation environments
Publications at top venues (NeurIPS, ICML, ICLR, CVPR, CoRL, RSS)
WHY JOIN OUR AI RESEARCH TEAM AT SNOWFLAKE?
This is a rare opportunity to define a new research direction from the ground up. You won't be maintaining existing systems or iterating on someone else's roadmap. You'll be building the foundational training platform for physical AI at a company with the infrastructure, data scale, and research ambition to make it real. Our team already ships frontier models (Arctic LLM, Arctic Inference) and production agentic systems (Snowflake Intelligence). You'll have the resources of a platform company with the pace and autonomy of a research lab
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
8+ years of relevant experience in machine learning engineering, AI research, or a closely related field (or equivalent experience)
Deep expertise in at least two of the following: representation learning, world models, reinforcement learning, generative modeling, robotics/embodied AI, or scientific ML
Hands-on experience training large-scale models (vision, language, or multimodal) with distributed compute
Strong software engineering fundamentals: system design, performance optimization, and production-quality code
Demonstrated ability to drive cross-team technical initiatives with ambiguity and limited direction
Track record of translating research ideas into working systems at scale
MS or Ph.D. in Computer Science, Machine Learning, Robotics, Physics, or a related field, or equivalent experience