Pantograph is training general models that start by watching internet-scale video and end up on robots. We think the path to capable robots runs through general intelligence rather than narrow, robot-specific skills. We're scaling simple methods across video games, real-world video, and our own fleet of affordable, durable robots. We're looking for research scientists who want to scale simple methods across the largest datasets available. You might be a good fit if you: • Have experience with one or more of: • Large-scale pre-training (video, multimodal, image, or language) • Self-supervised, goal-conditioned, or unsupervised RL • Robotics models, especially those trained on large-scale data • Video generation or other large-scale sequence modeling over high-dimensional observations (e.g. pixels) • Have trained models on large GPU clusters and are comfortable working with Kubernetes • Believe simple methods that scale beat complicated ones that don't, and reach for the simplest thing that could work • Strive to find simple, expressive metrics and measure them accurately • Value scientific integrity and seek to understand the true effect of different interventions Nice to have: • Experience with JAX • Interest in problems adjacent to the critical path — new modalities, alternatives to text for reasoning, pixel-space modeling, or automating research itself • A strong background in proof-based mathematics, including topics such as: • Measure-theoretic probability • Stochastic processes • Optimization theory We care much more about what you can do than any specific credential. We're interested in published work or lab experience, but equally in strong open-source contributions or personal projects. If you're excited about scaling general models that learn from and act in the real world, we'd love to talk.