Member of Technical Staff, Research Scientist

pantograph.com; San Francisco (CA); Vor Ort; USD 150.000 - 230.000; Vollzeit

Zusammenfassung

Pantograph in San Francisco is seeking research scientists to scale simple methods across large video and multimodal datasets, with applications to robotics. You will work on training regimes that generalize from internet-scale video to real-world robots and fleets.

We value candidates who have built models on large GPU clusters, are comfortable with Kubernetes, and favor straightforward, scalable approaches with clear, measurable metrics.

Qualifikationen

• Experience with large-scale pre-training across video, multimodal, image, or language.
• Experience in self-supervised, goal-conditioned, or unsupervised RL.
• Background in robotics models trained on large-scale data.
• Experience with large GPU clusters and Kubernetes.
• Preference for simple, scalable methods with clear metrics.

Aufgaben

• Scale simple methods across large datasets and domains.
• Collaborate on video, real-world video, and robotics projects.
• Develop and evaluate expressive metrics to measure improvements.
• Share results through open-source contributions or publications.

Kenntnisse

Large-scale pre-training

Self-supervised RL

Robotics models

Video sequence modeling

Scalable methods

Expressive metrics

Tools

Kubernetes

JAX

Jobbeschreibung

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.