C++/CUDA Systems Engineer
Santa Monica, CA

We are seeking a Senior C++/CUDA Systems Engineer, Surgical Robotics Platform for our client in You will work on a C++/CUDA-based runtime for real-time surgical imaging and perception, operating in a live surgical environment where latency, reliability, and determinism are critical.

This role sits within the AI team but focuses on production systems: GPU inference pipelines, device orchestration, and system integration. You will own components that span from hardware-facing interfaces to high-performance compute pipelines, and ensure they operate cohesively under real-time constraints.

Key Responsibilities:

• Design and maintain multi-threaded C++ pipelines for real-time model inference, integrated with robotic control and perception systems
• Implement scheduling and resource management across CPU/GPU to meet strict latency and throughput constraints
• Develop infrastructure for coordinating physical devices (cameras, sensors, motion systems), including sequencing, configuration, and fault handling
• Define and implement system interfaces (APIs, messaging, IPC) to integrate subsystems into a coherent runtime
• Build and maintain automated testing at component and system levels, including simulation and hardware-in-the-loop where applicable
• Contribute to profiling, observability, and debugging tooling for performance-critical systems

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, or related field
• 3+ years of experience writing production C++ in systems, robotics, or embedded environments
• Strong C++17/20 fundamentals (multithreading, memory management, concurrency patterns)
• Experience with CUDA or GPU-accelerated compute pipelines
• Experience building or maintaining real-time or latency-constrained systems
• Familiarity with logging, diagnostics, and performance profiling in production environments

Preferred Qualifications:

• Experience in medical devices, robotics, or other safety-critical systems
• Familiarity with ML inference frameworks (e.g., TensorRT, ONNX Runtime)
• Experience integrating and debugging physical hardware (cameras, motors, sensors)
• Familiarity with inter-process communication (DDS, shared memory, or similar)
• Experience with performance profiling tools (Nsight Systems, perf, nvprof, or equivalent)