Were 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 dont, 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. Were interested in published work or lab experience, but equally in strong open-source contributions or personal projects. If youre excited about scaling general models that learn from and act in the real world, wed love to talk.
