Research Engineer (Robotics)

Location: San Mateo, United States — on-site

Company

hirify.global builds data collection systems, large-scale data infrastructure, and software tools that support training data for general-purpose robotics and foundation models.

What you will do

• Turn research prototypes into reliable, production-grade systems running on real hardware.
• Harden perception pipelines such as pose estimation, SLAM, and calibration for real-time embedded execution.
• Profile and optimize CPU-, memory-, and GPU-intensive workloads using performance-analysis tools and microbenchmarks.
• Write, profile, and debug CUDA kernels for low-level acceleration.
• Integrate research outputs into production code with testing, error handling, observability, containerization, and packaging.
• Work with researchers and infrastructure engineers to balance accuracy, latency, resource usage, and deployment requirements.

Requirements

• 3+ years of industry experience in software engineering focused on systems, performance, or production ML.
• Strong C++ proficiency, including modern C++17/20, memory management, and performance-conscious coding.
• Experience writing, profiling, and debugging CUDA GPU kernels.
• Experience with CPU performance optimization, including profiling, cache behavior, SIMD, and latency reduction.
• Proficiency in Python and familiarity with PyTorch or TensorFlow for reading and modifying research code.
• Comfort with Linux systems, build systems, debugging tools, and containerization.

Nice to have

• Experience shipping research or prototype code in production.
• Background in real-time or embedded systems, perception, computer vision, or robotics.
• Experience optimizing model inference with TensorRT, ONNX Runtime, or similar tools.
• Understanding of the lifecycle from research notebooks to containerized, monitored production services.
• Experience working in 0→1 environments.

Culture & Benefits

• Fast-paced work across perception, ML, and data-infrastructure teams.
• Opportunity to move between teams based on the highest-priority technical needs.
• Close collaboration with researchers to translate algorithmic intent into production systems.