Research Member of Technical Staff - Video Generation Modeling

About the Company

Rhoda AI is building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up.

Responsibilities

• 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 and downstream robot task performance
• Run rigorous ablations and benchmarking to understand what drives model quality at scale
• Collaborate with data, post-training, and training systems teams to translate research into working systems
• Publish and present work at top-tier ML and robotics venues

Requirements

• Strong background in large-scale generative modeling (video generation or LLM pretraining)
• Hands-on experience training large generative models from scratch at scale
• Deep understanding of autoregressive modeling, causal architectures, and scaling behavior
• Fluency with PyTorch
• Ability to design experiments, interpret results, and iterate quickly
• Comfort operating in a fast-moving, ambiguous startup environment

Preferred Qualifications

• PhD in ML, CS, Robotics, or a related field
• Strong publication record at NeurIPS, ICML, ICLR, CVPR, or CoRL
• Prior work on video generation models (diffusion transformers, world models, etc.)
• Experience with large-scale autoregressive language model pretraining
• Familiarity with web-scale video datasets and curation pipelines
• Experience with distributed training and multi-node infrastructure