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Healthcare

Member of Technical Staff, Research Engineer

San Mateo, CAOnsiteFull TimeStaffPosted 2 months ago

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Required skills

  • research prototype
  • real-time execution
  • embedded platforms
  • CPU
  • memory
  • GPU
  • CUDA kernels
  • performance-critical code
  • error handling
  • observability

Nice to have

  • real-time systems
  • embedded systems
  • latency constraints
  • resource constraints
  • perception
  • computer vision

Role summary

XDOF is seeking a Research Engineer to bridge the gap between research prototypes and production-grade systems, focusing on data collection, exabyte-scale data warehousing, and software toolchains for general-purpose robots. The role involves hardening research pipelines for reliable, real-time execution on embedded platforms, optimizing performance-critical code across CPU, memory, and GPU, writing CUDA kernels, integrating research outputs with proper testing and observability, and containerizing workloads. Candidates should have 3+ years of industry experience in software engineering with a focus on systems, performance, or production ML, strong C++ proficiency, CUDA programming experience, CPU performance optimization skills, and familiarity with Python and ML frameworks. Experience with real-time/embedded systems, computer vision, robotics, or model inference optimization is a plus.

Research Engineer

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.

What You’ll Do

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 and debugging CUDA kernels for low-level acceleration of compute-heavy workloads

  • integrating research outputs into the production codebase with proper testing, error handling, and observability

  • containerizing and packaging workloads (Docker) so they can be scaled and deployed by the infrastructure team

  • understanding and leveraging the infrastructure team’s orchestration and compute systems to hand off production-ready workloads cleanly

  • working with researchers to understand algorithmic intent and make informed tradeoffs between accuracy, latency, and resource usage

About You

Baseline skills:

  • 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

You might be a good fit if you:

  • have taken research or prototype code and shipped it in a production system

  • have worked on real-time or embedded systems where latency and resource constraints matter

  • have experience with perception, computer vision, or robotics systems

  • have optimized model inference for deployment (TensorRT, ONNX Runtime, or similar)

  • understand the full lifecycle from research notebook to containerized, monitored production service

  • 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!

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