We9re looking for a research engineer to help us train increasingly capable models across enormous and diverse datasets.
You9ll work across the boundary between research and engineering: implementing new ideas, scaling experiments across large GPU clusters, building the systems that let us iterate quickly, and figuring out why things aren9;t working. The work spans large-scale model training, multimodal representation learning, reinforcement learning, data processing, evaluation, and the infrastructure required to support all of it.
You might be a good fit if you:
- Have trained models across large GPU clusters and are comfortable working with Kubernetes
- Have built or operated complex distributed systems
- Have worked with multi-terabyte or multi-petabyte datasets
- Are comfortable with large-scale data processing tools
- Care deeply about observability and collect enough metrics to understand what every part of a system is doing
- Are comfortable moving between research code and production-quality systems
- Like running experiments, getting surprising results, and digging in until you understand why
- Move quickly and reach for simple approaches before complicated ones
Nice to have:
- Experience with JAX
- Experience writing CUDA kernels or otherwise optimizing GPU workloads
- Low-level Linux or kernel programming experience
- Experience with large-scale video or multimodal datasets
- Experience building training or evaluation infrastructure
- Experience with distributed training
- Experience deploying models into real-world systems, especially robotics
We care much more about what you9ve built than any specific credential. We9re a small, fast-moving team working together in person in San Francisco. If you9re excited about architecting novel systems at unprecedented scale, we9d love to talk.
