Startups · AI

Member of Technical Staff, Research Engineer

XDOF · San Mateo On-site · On-site

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About XDOF

Defining motion for autonomous systems. Backed by a16z.

About the role

Research engineers take prototype code and make it production-grade. Sample projects include: taking a research perception pipeline (pose estimation, SLAM, calibration) and hardening it for reliable, real-time execution on embedded platforms

What they're looking for

  • 3+ years of industry experience in software engineering with a focus on systems, performance, or production ML
  • strong C++ proficiency, including modern C++ (C++17/20), memory management, and performance-conscious coding patterns
  • CUDA programming experience: ability to write, profile, and debug GPU kernels
  • experience with CPU performance optimization: profiling, cache behavior, SIMD, latency reduction
  • proficiency with Python and familiarity with ML frameworks (PyTorch, TensorFlow) at the level needed to read and modify research code
  • comfort with Linux systems, including build systems, debugging tools, and containerization
More about this role

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.

Our research teams move fast and produce breakthrough work, but research code and production code are different things. We’re looking for a Research Engineer to bridge that gap: someone who can read a research prototype, understand it deeply, and turn it into something that runs reliably at scale on real hardware. You can expect to float across teams to wherever the highest-priority needs are, across perception, ML, and data infrastructure.

Research engineers take prototype code and make it production-grade. Sample projects include:

taking a research perception pipeline (pose estimation, SLAM, calibration) and hardening it for reliable, real-time execution on embedded platforms

profiling and optimizing performance-critical code at the CPU, memory, and GPU level using tools like perf, NSight, and custom microbenchmarks

writing...

Read the full posting on XDOF's site ↗

Robotics

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