Vision-Language Robotics Research Scientist

XDOF; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

XDOF is hiring a Research Scientist to lead the exploration at the intersection of vision-language models and robot learning. You will guide a project on what data a model needs to reason about and act in the physical world, testing hypotheses with prototyping and experiments that demonstrate impact.

The role emphasizes representation learning with a focus on egocentric video and structured annotations, collaborating with partner labs and pushing the frontier of embodied AI in robotics.

Qualifications

• 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 in 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.

Responsibilities

• 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.

Education

PhD in CS/ML/Robotics

Tools

PyTorch

Distributed training

Job description

XDOF is hiring a Research Scientist to lead the exploration at the intersection of vision-language models and robot learning. You will guide a project on what data a model needs to reason about and act in the physical world, testing hypotheses with prototyping and experiments that demonstrate impact.

The role emphasizes representation learning with a focus on egocentric video and structured annotations, collaborating with partner labs and pushing the frontier of embodied AI in robotics.