Research Scientist / Engineer - Video Generation Modeling

Benefits

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Deadline

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About this role

Research Scientist / Engineer - Video Generation Modeling Palo Alto, California, United States At Rhoda AI, we're 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. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $400M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality. We're 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. You'll 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 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

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