Staff Research Scientist (Exotic AI)

Join our AI Research team as a Staff Research Scientist, Exotic AI. In this role, you will build the next-generation training and learning platform for physical AI, focusing on representation learning, world models, and policy optimization. You will design and build scalable training infrastructure, develop latent world models, architect action/policy model pipelines, and build generative simulator frameworks. You will also lead cross-team technical decisions, drive research-to-production pathways, and contribute to the broader research community.

• Concevoir et construire une infrastructure d'entraînement évolutive pour les modèles de représentation.
• Développer des modèles du monde latent qui apprennent la dynamique de l'environnement par le biais de simulations imaginées.
• Diriger les décisions techniques inter-équipes sur les frameworks d'entraînement, les pipelines de données et l'infrastructure d'évaluation des modèles.
• Experience with latent dynamics modeling, model-based RL, or physics-informed neural networks (GraphCast, FourCastNet, AlphaFold-style architectures)
• Hands-on experience training large-scale models (vision, language, or multimodal) with distributed compute
• Contributions to open-source ML frameworks or foundation model training codebases
• MS or Ph.D. in Computer Science, Machine Learning, Robotics, Physics, or a related field, or equivalent experience
• Experience building controllable video generation or neural simulation environments
• Demonstrated ability to drive cross-team technical initiatives with ambiguity and limited direction
• 8+ years of relevant experience in machine learning engineering, AI research, or a closely related field (or equivalent experience)
• Publications at top venues (NeurIPS, ICML, ICLR, CVPR, CoRL, RSS)
• Deep expertise in at least two of the following: representation learning, world models, reinforcement learning, generative modeling, robotics/embodied AI, or scientific ML
• Track record of translating research ideas into working systems at scale
• BONUS POINTS FOR THE FOLLOWING:
• Background in scientific/structured models (molecular modeling, materials science, weather/climate)
• Strong software engineering fundamentals: system design, performance optimization, and production-quality code