Staff Research Scientist, Exotic AI

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)