Research Engineer

xdof, Inc.; San Mateo, Northern (CA, KY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

XDOF, Inc. in California is seeking a Research Engineer to bridge research prototypes to production-grade code running reliably on embedded hardware.

You’ll move across perception, ML, and data infrastructure to ship real-time systems and optimize performance from CPU to GPU. Ideal candidates have strong C++ (modern standards), CUDA, Python for ML, and experience profiling, debugging, and containerizing workloads with Docker.

Qualifications

• 3+ years of industry software engineering with a focus on systems, performance, or production ML.
• Strong C++ proficiency (C++17/20) with memory management and performance-conscious coding patterns.
• CUDA programming experience: ability to write, profile, and debug GPU kernels.
• Proficiency with Python and familiarity with ML frameworks (PyTorch, TensorFlow) to read/modify research code.
• Comfort with Linux systems, including build systems, debugging tools, and containerization.

Responsibilities

• Making research prototype code production-grade for reliable execution on embedded hardware.
• Hardening a perception pipeline for real-time performance on embedded platforms.
• Profiling and optimizing performance across CPU, memory, and GPU levels.
• Writing and debugging CUDA kernels for acceleration of compute-heavy workloads.
• Integrating research outputs into production code with testing, error handling, and observability.
• Containerizing workloads (Docker) so they can be deployed by the infrastructure team.
• Collaborating with researchers to balance accuracy, latency, and resource usage.

Skills

C++ (modern)

CUDA

Python for ML

Linux systems

Performance profiling

TensorRT/ONNX

Tools

Docker

Nsight

CUDA profiling tools

Job description

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!