Research Scientist

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.