Were looking for Research Scientists and Research Engineers to push the frontier of large-scale pre-training for our video action model. Our approach formulates robot control as video prediction - we pre-train causal video generation models on web-scale video data, then adapt them to predict robot actions from real-world demonstrations. Youll work on the core architectures, training objectives, and scaling strategies that determine how well our models learn from internet-scale video. We hire across levels - from senior to staff - and welcome both research-track and engineering-track candidates.
What Youll Do- Design and train large-scale causal video generation models on web-scale video data
- Develop and validate training objectives, model architectures, and data mixtures for video prediction at scale
- Research scaling laws and data efficiency for web-scale video pretraining
- Investigate what properties of web video transfer most effectively to robotic control and action prediction
- Build systematic evaluations to measure video generation quality, long-horizon prediction fidelity, and downstream robot task performance
- Run rigorous ablations and benchmarking to understand what drives model quality at scale
- Collaborate closely with data & evaluation, post-training, and training systems teams to translate research ideas into working systems
- Publish and present work at top-tier ML and robotics venues (especially valued for RS track)
What Were Looking For- Strong background in large-scale generative modeling - either video generation (autoregressive video models, diffusion transformers, causal video architectures) or language model pretraining (LLMs, autoregressive transformers at scale)
- Hands-on experience training large generative models from scratch at scale
- Deep understanding of autoregressive modeling, causal architectures, and scaling behavior
- Fluency with modern ML frameworks (PyTorch required; JAX a plus)
- Ability to design experiments, interpret results, and iterate quickly
- Strong research taste: ability to identify high-leverage questions and cut through noise
- Comfort operating in a fast-moving, ambiguous startup environment
- Staff-level candidates are expected to define technical direction and drive research strategy independently; senior/MTS candidates execute complex projects with strong fundamentals and growing scope
Nice to Have (But Not Required)- PhD in ML, CS, Robotics, or a related field - or equivalent research/industry experience
- Strong publication record at NeurIPS, ICML, ICLR, CVPR, CoRL, etc. (especially valued for RS track)
- Prior work specifically on video generation models (autoregressive video, diffusion transformers, world models, or causal video architectures)
- Experience with large-scale autoregressive language model pretraining and scaling
- Familiarity with web-scale video datasets and video data curation pipelines
- Prior work connecting video generation to control, action prediction, or robotic learning
- Familiarity with distributed training and multi-node infrastructure
Why This Role- Work on a fundamentally different approach to robot learning - web-scale video pretraining rather than robot-data-only VLA models
- Your models give our robots the ability to understand and predict the visual world from internet-scale supervision
- Direct collaboration with data, post-training, and deployment teams with no silos
- High ownership and fast iteration in a small, elite team
