Senior Physical AI Computer Vision Engineer
Role details
Job location
Tech stack
Job description
We are seeking a Senior Physical AI Software Engineer to develop next-generation software for intelligent edge systems that combine embedded computing, real-time processing, computer vision, and AI.
In this role, you will design, develop, and optimize software that enables machines to perceive, analyze, and respond to their environments with deterministic, low-latency performance. You will build software spanning embedded Linux and RTOS platforms while integrating AI inference, computer vision, sensor processing, and real-time control across AMD's embedded computing portfolio.
Working closely with software, AI, FPGA, silicon, and systems engineers, you will help deliver scalable Physical AI solutions for robotics, industrial automation, automotive, machine vision, aerospace, and other intelligent edge applications.
This position provides an opportunity to work across the complete software stack-from low-level platform software through AI-enabled applications-leveraging heterogeneous computing architectures including CPUs, GPUs, FPGAs, and dedicated AI accelerators., * Design, develop, and maintain embedded software for AI-enabled edge computing platforms.
- Develop software for Linux and Real-Time Operating Systems (RTOS) supporting deterministic, low-latency execution.
- Design and implement real-time software for perception, sensor processing, control, and autonomous decision-making.
- Develop and integrate computer vision, image processing, and AI inference applications for real-world deployment.
- Build and optimize high-performance image, video, and sensor processing pipelines.
- Optimize software across heterogeneous computing architectures including CPUs, GPUs, FPGAs, and AI accelerators.
- Collaborate with AI, FPGA, hardware, systems, and platform teams to deliver integrated Physical AI solutions.
- Support platform bring-up, debugging, validation, and system integration.
- Analyze and optimize latency, throughput, memory utilization, power efficiency, and overall system performance.
- Contribute to software architecture, technical documentation, coding standards, and engineering best practices.
- Develop demonstrations, reference applications, and customer enablement solutions showcasing AMD embedded technologies., AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
Requirements
You are passionate about building software that bridges embedded systems, AI, and real-world intelligent machines. You thrive solving complex engineering challenges involving real-time processing, perception, and system optimization.
You enjoy working across multidisciplinary teams and are comfortable developing software that spans embedded Linux, RTOS, computer vision, AI inference, and heterogeneous computing architectures. You are driven by performance, scalability, and delivering robust software that powers next-generation intelligent systems., * Experience with RTOS platforms such as QNX, GreenHills, or similar real-time operating systems.
- Experience designing software for low-latency, safety-critical, or time-sensitive embedded applications.
- Experience with AMD Vitis AI or equivalent AI deployment frameworks.
- Experience with AMD Ryzen Embedded, EPYC Embedded, Versal AI Edge, Zynq UltraScale+, Kria, or similar edge computing platforms.
- Understanding of FPGA-based acceleration and hardware/software co-design methodologies.
- Experience with AI inference deployment using ONNX, PyTorch, TensorFlow, or related frameworks.
- Experience with ROS 2 and robotics software architectures.
- Understanding of perception systems including cameras, radar, lidar, and sensor fusion technologies.
- Familiarity with graphics and visualization technologies such as OpenGL, Vulkan, or Wayland.
- Experience with Linux kernel, BSP, device driver, or low-level platform software development.
- Understanding of heterogeneous computing architectures combining CPUs, GPUs, FPGAs, and dedicated AI accelerators.
- Experience with performance profiling, optimization, and benchmarking on multicore systems.
- Knowledge of networking, middleware, and distributed edge computing systems.
- Experience within robotics, automotive, industrial automation, aerospace, machine vision, or related embedded systems domains.