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XDOF is building the foundational infrastructure for robotics—the data collection systems, data warehouses, and software toolchains that enable frontier labs to train and deploy general-purpose robots at scale. High-quality training data is the critical bottleneck in the field, and XDOF is solving that problem.
As a Robotics Platform Engineer, you will own the systems that get learned robot policies running on real hardware and measure their performance. This is a 0→1 role where you'll build both the technical infrastructure and the product surfaces that partners use to deploy and evaluate models.
Key responsibilities include:
- Deploying and running learned robot policies (VLAs, imitation learning, DAgger workflows) 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 product features that let 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 to enable rapid station onboarding and consistent deployments
- Bridging on-robot systems with cloud-side compute, storage, and orchestration
This role sits at the intersection of robotics, ML deployment, and product engineering. You'll work in a fast-paced, mission-driven environment where the work directly impacts how the robotics field advances.
REQUIREMENTS:
Baseline:
- Bachelor's degree or equivalent experience in Computer Science, Robotics, or 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
Preferred:
- Deployed imitation learning, reinforcement learning, or VLA policies on physical robots
- Built evaluation or benchmarking infrastructure for robotic systems or ML models
- Experience with DAgger or other interactive data collection and policy refinement loops
- Experience with ROS/ROS2 and robotics middleware at the integration level
- Built product features or partner-facing tools on top of technical infrastructure
- Comfortable working in 0→1 environments
- Mission-driven passion for robotics
