Research Engineer
Research Engineer who productionizes robotics, perception, and machine-learning prototypes for reliable, real-time execution on hardware. Requires 3+ years of systems or production ML experience, strong modern C++, CUDA, Python, Linux, and performance optimization skills.
About the job
Responsibilities
- Convert research prototypes into reliable, production-grade software for real hardware and large-scale deployment.
- Harden perception pipelines, including pose estimation, SLAM, and calibration, for reliable real-time execution on embedded platforms.
- Profile and optimize performance-critical code at the CPU, memory, and GPU levels using tools such as
perf, NSight, and custom microbenchmarks. - Write and debug CUDA kernels for low-level acceleration of compute-intensive workloads.
- Integrate research outputs into production codebases with testing, error handling, and observability.
- Containerize and package workloads with Docker for infrastructure-team deployment and scaling.
- Use orchestration and compute systems to hand off production-ready workloads.
- Collaborate with researchers to understand algorithmic intent and balance accuracy, latency, and resource usage.
Requirements
- 3+ years of industry experience in software engineering focused on systems, performance, or production machine learning.
- Strong C++ proficiency, including modern C++17/20, memory management, and performance-conscious coding.
- Experience writing, profiling, and debugging CUDA kernels.
- Experience with CPU performance optimization, including profiling, cache behavior, SIMD, and latency reduction.
- Proficiency in Python and familiarity with PyTorch or TensorFlow sufficient to read and modify research code.
- Comfortable working with Linux systems, build systems, debugging tools, and containerization.
Nice-to-haves
- Experience shipping research or prototype code in production systems.
- Experience with real-time or embedded systems involving latency and resource constraints.
- Experience with perception, computer vision, or robotics systems.
- Experience optimizing model inference for deployment with TensorRT, ONNX Runtime, or similar tools.
- Understanding of the lifecycle from research notebook to containerized, monitored production service.
- Comfort working in 0-to-1 environments.
Compensation
- Compensation was not specified in the posting.
Skills
C++, CUDA, Python, Linux, Docker, PyTorch, TensorFlow, Computer Vision, Robotics, Slam, TensorRT, Onnx Runtime, Simd, Nsight, Performance Optimization
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