Staff Research Scientist, Exotic AI

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

Staff Research Scientist, Exotic AI at Snowflake will build the next-generation training and learning platform for physical AI, focusing on models that perceive, reason about, and act within structured environments. This involves designing and building scalable training infrastructure for representation models, developing latent world models, architecting action/policy model pipelines, building generative simulator frameworks, and developing multimodal generative model capabilities. The role also involves leading cross-team technical decisions and driving research-to-production pathways.

What you'd actually do

  1. Design and build scalable training infrastructure for representation models (e.g., contrastive and self-supervised approaches like CLIP/SigLIP, DINO/MAE, and joint-embedding predictive architectures)
  2. Develop latent world models that learn environment dynamics through imagined rollouts, enabling model-based reasoning and planning (Dreamer-style, I-JEPA/V-JEPA families)
  3. Architect and implement action/policy model pipelines, including vision-language-action models and diffusion-based policy learning
  4. Build generative simulator frameworks that produce controllable, physically plausible future states (video world models in the spirit of Cosmos/Genie/Sora)
  5. Develop multimodal generative model capabilities that fuse visual, language, and structured inputs for downstream reasoning and decision-making

Skills

Required

  • 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
  • MS or Ph.D. in Computer Science, Machine Learning, Robotics, Physics, or a related field, or equivalent experience

Nice to have

  • 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)

What the JD emphasized

  • greenfield effort
  • define its technical direction from the day one
  • Lead cross-team technical decisions
  • Drive research-to-production pathways
  • Track record of translating research ideas into working systems at scale

Other signals

  • building the foundational training platform for physical AI
  • greenfield effort at the intersection of representation learning, world models, and policy optimization
  • design and build scalable training infrastructure for representation models
  • develop latent world models
  • architect and implement action/policy model pipelines
  • build generative simulator frameworks
  • develop multimodal generative model capabilities