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Open nowPosted 2 days ago

Member of Technical Staff, Research Scientist

Pantograph9 open roles

Where
San Francisco
Work mode
On site
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Your applicationOpen nowMember of Technical Staff, Research ScientistPantograph · San Francisco

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The clock on this job

Early applications get read.

13.9% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.9%7 days
  4. 22.4%14 days
  5. 38.1%30 days
  6. 59.0%60 days
This job: posted 2 days ago

The posting

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.

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