Senior Systems GPU Engineer – AI & Robotics

Intuitive•San Francisco, CA

14h•$160,300 - $271,400•Onsite

About The Position

As a Senior Systems GPU Engineer – AI & Robotics, you will be responsible for iteratively improving and integrating high performance robotic AI with MLE product teams. You will work alongside research, SW/ HW/ ML engineering, regulatory, controls and clinical teams to translated early pre-product ML algorithms into performance optimized, robust, validated and scalable medical device products and infrastructures, including edge and cloud.

Requirements

Master’s degree or higher in Computer Science, Computer Engineering, or related technical field; advanced degrees preferred.
6+ years of embedded systems software development (or equivalent experience), including supporting real-time, high-performance embodied AI, perception systems
Expert in GPU Compute API – CUDA, OpenCL
Proficiency in multiple programming languages (e.g., C, C++, Python, Bash) and building real-time, multi-threaded applications.
Experience with industrial embedded operating systems (e.g., QNX, Yocto), interprocess communication, hardware interfaces
Experience with machine learning models, and frameworks such as PyTorch, TensorFlow, etc
Experience in robotics, including motion control, perception, sensor fusion, path planning, and optimizing models for platforms with limited resources.
Strong problem-solving, adaptability to new technologies, and excellent written and verbal communication skills.

Nice To Haves

End-to-end GPU performance engineering from the profiler to systems analysis, including experience in distillation, parameter-efficient fine-tuning and quantization and hardware acceleration techniques.
Linux systems programming and optimization experience.
Exposure to virtualization techniques and cloud platform solutions.

Responsibilities

GPU & Model Performance & Optimization: Advanced skills using performance profiling, tracing, and debugging tools (e.g. NVIDIA Nsight Systems, Nsight Compute, to identify code or hardware bottlenecks and implement improvements
System Architecture & Integration: Design and optimize software interfaces between robotic subsystems, ensuring seamless integration between edge AI, firmware, OS, hardware, and cloud services. You will oversee the deployment and continuous improvement of these systems to ensure high reliability and performance.
Linux Systems & Virtualization: Development of Linux kernel internals, device drivers, memory management, and containerized GPU runtime environments (e.g., Docker, NVIDIA Container Toolkit, Kubernetes)
Project Ownership & Strategy: Lead complex, end-to-end embodied intelligence projects, making critical architectural decisions and technical trade-offs. You will define the strategic roadmap for the robotics platform—from foundational models to real-time onboard inference—while serving as a core contributor to team planning and design reviews.
Cross-Functional Collaboration: Partner with motion controls, perception, and hardware teams during early-stage exploration to address clinical, functional, and safety requirements. You will work closely with the research and product development teams to integrate perception, planning, navigation, and multimodal ML models onto edge platforms.
Iterative Development: Refine designs by balancing technical feasibility with schedules and resource constraints. You will drive the transition from broad technical exploration to deep-dive implementation as the product matures.