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.
Skills Required
- Experience with large-scale pre-training (video, multimodal, image, or language)
- Experience with self-supervised, goal-conditioned, or unsupervised reinforcement learning
- Experience with robotics models, especially those trained on large-scale data
- Experience with video generation or large-scale sequence modeling over high-dimensional observations (pixels)
- Experience training models on large GPU clusters
- Comfortable working with Kubernetes
- Experience with JAX
- Interest in adjacent problems (new modalities, alternatives to text for reasoning, pixel-space modeling, automating research)
- Strong background in proof-based mathematics (measure-theoretic probability, stochastic processes, optimization theory)
- Published work, lab experience, strong open-source contributions, or personal projects demonstrating capability
What We Do
Pantograph is a San Francisco-based public benefit corporation and research lab developing generally intelligent robots. It combines scalable machine-learning methods with internet-scale video and real-world robotic data to train models that learn through exploration, failure, and repetition. The company also designs affordable, durable robots intended for broad deployment, aiming to make general-purpose robotics practical and support beneficial applications across physical environments.
