At XDOF, we're at an inflection point. Frontier labs are racing to build general-purpose robots, and high-quality training data is the bottleneck. We're building the foundation behind the foundation models – the data collection systems, operational capability, exabyte-scale data warehouse, and software toolchain – to help our partners drive the field forward.
As a Research Engineer at XDOF, you will design, build, and deploy real world robot learning systems, working across manipulation, locomotion, and control to push robots from raw hardware to fully functional systems.
This is a deeply hands on role. You will own systems end to end, from simulation and model training to deployment on real robots, and you will help define the technical foundations that enable large scale robot learning.
Baseline skills:
Bachelor's degree or equivalent experience in Robotics, Computer Science, or a related field
Strong experience with Python and modern deep learning frameworks such as PyTorch or JAX
Full stack experience across robot learning, including data processing, model training, and deployment
Experience designing, implementing, and deploying robot learning systems in research or production environments
Strong understanding of both classical robotics techniques and learning based approaches, and the trade offs between them
You might be a good fit if you:
Can take a robotics system from 0 to 1 and iterate quickly based on real world feedback
Have hands on experience in one or more of the following areas: robot manipulation, locomotion, navigation algorithms, imitation learning, generative modeling, and/or reinforcement learning
Are comfortable working across simulation and real hardware, including high degrees of freedom manipulators and mobile robots
Are obsessed with state of the art techniques, tools, and research, and enjoy pushing systems beyond what already exists
Thrive in fast paced, ambiguous environments where solutions are not predefined
Are mission driven and passionate about robotics: work at XDOF is fast paced and constant. We hope you love what you're going to be doing, because you'll be doing a lot of it.