Staff Research Scientist, Exotic AI
Build next-generation training infrastructure for physical AI models that perceive, reason, and act in structured environments. Lead development of representation models, latent world models, and policy optimization systems.
Responsibilities
- 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)
- Develop latent world models that learn environment dynamics through imagined rollouts, enabling model-based reasoning and planning (Dreamer-style, I-JEPA/V-JEPA families)
- Architect and implement action/policy model pipelines, including vision-language-action models and diffusion-based policy learning
- Build generative simulator frameworks that produce controllable, physically plausible future states (video world models in the spirit of Cosmos/Genie/Sora)
- Develop multimodal generative model capabilities that fuse visual, language, and structured inputs for downstream reasoning and decision-making
- Lead cross-team technical decisions on training frameworks, data pipelines, and model evaluation infrastructure
- Drive research-to-production pathways, translating prototype systems into reliable, performant platform capabilities
- Contribute to the broader research community through publications, open-source releases, and collaboration with academic partners
Requirements
- 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
Nice-to-Haves
- 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)
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