About the role
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.