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XDOF

Data pipelines and annotation for robotics

Member of Technical Staff - Software Generalist

Software EngineeringUpdated on Nov 13
Full-timeSan Mateo, CA, USA$155k - $270k /yrHybrid

About the Job

Requirements

  • Strong programming skills in Python or C++.
  • Ability to ramp up quickly in unfamiliar codebases, tools, and problem domains.
  • Experience building and shipping software in fast-moving environments where the role was not clearly defined.
  • Experience working across multiple domains, including backend, frontend, data, infrastructure, or scripting, without needing expertise in all of them.
  • Experience building systems that interact with physical hardware or manage compute-heavy workloads.
  • Experience with AI, robotics, or large-scale data systems.

Responsibilities

  • Build backend services and application programming interfaces for the robotics platform and customer-facing product.
  • Build the orchestration layer for workflows that use real robotic hardware.
  • Develop and implement the architecture for a real-to-simulation pipeline.
  • Script integrations between hardware systems, cloud infrastructure, and software services.
  • Work on whichever generalist engineering problems have the highest impact.

Desired Qualifications

  • Experience working in real-time systems, robotics infrastructure, simulation, rendering, 3D engines, or streaming systems.
  • Experience working with sensor systems, including video encoding, camera data, or perception systems.
  • Familiarity with distributed systems.

About the Company

XDOF

Data pipelines and annotation for robotics

XDOF builds data pipelines, data-collection tools, and annotation systems that frontier AI labs and robotics companies use to train robots to operate in the physical world. It tackles the data bottleneck in physical AI by combining teleoperation, simulation, and egocentric data collection, then layering in data cleaning, tooling, and annotation to prepare usable datasets. The company differentiates itself through its end-to-end data infrastructure for physical AI, its mix of teleoperation, simulation, and first-person data methods, and its Berkeley-founded background from 2024. Its goal is to enable robots to learn to operate in the real world by providing the pipelines and labels needed for training.

Company size
1-10
Company stage
Series A
Total funding
$78.3M
Headquarters
Berkeley, CA
Founded
2024

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