About the role
Frontier labs are racing to build general-purpose robots, and the bottleneck isn't compute. It's data. At xdof, we're building the foundation behind the foundation models: the data collection systems, annotation pipelines, exabyte-scale data infrastructure, and software toolchain that enable our partners to push the field forward.
We're hiring a Research Scientist to lead technical efforts at the intersection of vision-language models and robot learning. You will lead an exploratory project on what data a model actually needs in order to reason about and act in the physical world. This is an open question across the field, and the role is largely about finding the answer: studying how frontier models currently fail at embodied tasks, forming hypotheses about which kinds of structured annotations on egocentric video would close those gaps, prototyping them on our data, and running experiments that show whether they help.
The ideal candidate has a research background in representation learning, and some of the following: vision, robotics, and language models.
What You'll Do
- Develop and evaluate representations that bridge visual perception, language, and low-level robot action conditioned on different modalities Collaborate directly with partner labs to define data requirements and close the loop between data quality and downstream policy performance
- Stay current on the research frontier (VLAs, video foundation models, flow matching, DiT architectures, egocentric pretraining) and translate insights into new research and production systems
About You
Required
- PhD in Computer Science, Robotics, Machine Learning, or a related field from a top-tier program
- 3–7 years of research or applied research experience (industry or academic) in one or more of: vision-language models, video understanding, robot learning, or generative modeling
- Deep fluency in PyTorch; working knowledge of large-scale training infrastructure (distributed training, mixed precision, large batch workflows)
- Published work or demonstrable impact in VLMs/VLAs, video representation learning, imitation learning, or a closely related area
- Strong engineering fundamentals — you can design clean systems, not just run experiments