Research Member of Technical Staff- Video Generation Modeling

What You'll 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 We're 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

• 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