This listing does not state pay. Across 171 open member of technical staff listings that state pay, the middle half runs $200,000 to $270,000 and the median is $230,000.

Listed pay is what employers advertise, not what people are paid.

Member of Technical Staff salaries by state · Check your own pay

How AI is used in this kind of work

Among software developers, AI is already used for some of the tasks. Measured exposure for software developers: 29% (some).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

Check how this applies to your own job

Skills this job asks for

Say which you have and which you want to build. We use it to match you to jobs like this one.

About the job

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!