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.
We’re looking for a Robotics Platform Engineer to build the product and infrastructure that lets us and our partners deploy and evaluate learned robotis policies on real hardware. Examples of things you might work on are: deploying VLAs, imitation learning models, and DAgger workflows onto embodied systems, building the evaluation frameworks that measure how they perform, and shipping the product surfaces that make this accessible to partners.
What You’ll Do
Robotics platform engineers own the systems that get models running on robots and measure how well they work. Sample projects include:
deploying and running learned robot policies (VLAs, imitation learning, DAgger) on real hardware, including inference setup, on-robot tuning, and failure diagnosis
designing and building evaluation frameworks that benchmark policy performance on physical robots, with metric definition, result aggregation, and partner-facing reporting
building the product that lets partners configure, launch, and monitor evaluation runs and model deployments
building orchestration systems that schedule evaluation runs and model deployments across robot stations
integrating LLMs and foundation models into robot workflows for task specification, error recovery, or high-level planning
containerizing and packaging robot-side software stacks so new stations and deployments can be brought online quickly and consistently
bridging on-robot systems with cloud-side compute, storage, and orchestration
About You
Baseline skills:
bachelor’s degree or equivalent experience in Computer Science, Robotics, or a related field
3+ years of industry experience in software engineering, with exposure to robotics or ML deployment
strong Python skills and comfort working in Linux environments
experience deploying ML models to real systems, not just training them
familiarity with containerization (Docker) and building reproducible deployment workflows
You might be a good fit if you:
have deployed imitation learning, reinforcement learning, or VLA policies on physical robots
have built evaluation or benchmarking infrastructure for robotic systems or ML models
have experience with DAgger or other interactive data collection and policy refinement loops
have experience with ROS/ROS2 and understand robotics middleware at the integration level
have built product features or partner-facing tools on top of technical infrastructure
are very comfortable working in 0→1 environments
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!
Skills Required
- Bachelor's degree or equivalent experience in Computer Science, Robotics, or a related field
- 3+ years of industry experience in software engineering, with exposure to robotics or ML deployment
- Strong Python skills and comfort working in Linux environments
- Experience deploying machine learning models to real systems, not just training them
- Familiarity with containerization using Docker and reproducible deployment workflows
- Experience deploying imitation learning, reinforcement learning, or VLA policies on physical robots
- Experience building evaluation or benchmarking infrastructure for robotic systems or machine learning models
- Experience with DAgger or other interactive data collection and policy refinement loops
- Experience with ROS or ROS2 and robotics middleware integration
- Experience building product features or partner-facing tools on technical infrastructure
- Comfort working in zero-to-one environments
What We Do
XDOF is the infrastructure partner for the world's most ambitious robotics builders, developing the tools, data, and services that accelerate the future of physical AI. They build the foundational infrastructure for robotics foundation models, including data collection systems, annotation pipelines, and exabyte-scale data infrastructure, helping frontier labs build general-purpose robots by solving the critical bottleneck of high-quality training data.
