Role Description We are seeking a Senior Software Engineer to join our Physical AI team, developing next-generation AI-enabled systems for automotive, robotics, and intelligent edge applications. This role focuses on the deployment and optimization of advanced perception, computer vision, and AI workloads on high-performance adaptive computing platforms, including AMD Versal™ AI Edge Gen 2 devices. Working at the intersection of AI, robotics, embedded systems, and accelerated computing, you will help build intelligent systems capable of understanding and interacting with the physical world in real time. Key Responsibilities - Design, develop, and optimize software for Physical AI applications targeting AMD adaptive computing platforms. - Deploy and optimize AI inference workloads using Vitis AI and related toolchains. - Develop advanced perception pipelines for robotics, automotive, and intelligent edge applications. - Integrate AI models into high-performance embedded and edge computing environments. - Collaborate with hardware, FPGA, systems, and AI engineering teams to maximize application performance and efficiency. - Analyze and optimize end-to-end system performance, latency, throughput, and power consumption. - Support the development of computer vision, sensor fusion, and multimodal AI applications. - Develop and maintain software frameworks, SDKs, demonstrations, and reference implementations. - Contribute to technical documentation, best practices, and customer enablement activities. - Evaluate emerging AI models, frameworks, and deployment methodologies for Physical AI applications. Qualifications - Strong software development experience in C++, Python, and Linux environments. - Experience deploying and optimizing machine learning or deep learning models for edge devices. - Strong understanding of AI inference pipelines and performance optimization techniques. - Experience with computer vision, perception systems, or sensor processing applications. - Solid understanding of embedded systems and heterogeneous computing architectures. - Experience profiling and optimizing software performance across CPU, GPU, NPU, FPGA, or accelerator-based systems. - Strong debugging, problem-solving, and system integration skills. - Excellent written and verbal communication skills. - Ability to work effectively within multidisciplinary engineering teams. Preferred Experience - Experience with AMD Vitis AI or equivalent AI deployment frameworks. - Experience with AMD Versal AI Edge, Zynq UltraScale+, Kria, or other adaptive computing platforms. - Understanding of FPGA acceleration and hardware/software co-design methodologies. - Experience developing AI applications using ONNX, PyTorch, TensorFlow, or similar frameworks. - Familiarity with quantization, model optimization, pruning, and edge deployment techniques. - Experience with ROS 2 and robotics software architectures. - Understanding of perception systems including camera, radar, lidar, and sensor fusion pipelines. - Experience with graphics, visualization, or accelerated rendering technologies. - Familiarity with OpenGL, Vulkan, Wayland, or embedded graphics frameworks. - Experience with Linux kernel, device drivers, or low-level platform software development. - Experience with distributed AI, edge-to-cloud architectures, or heterogeneous computing systems. - Experience with robotics, automotive, aerospace, industrial automation, or other intelligent edge domains. Location San Jose, CA Benefits AMD benefits at a glance.

• Manage, optimize, and troubleshoot Nextflow pipelines for large-scale multi-omics workloads • Build and maintain CI/CD pipelines (Jenkins) for cloud infrastructure and scientific workflow deployments • Develop and maintain Infrastructure as Code (Terraform, Ansible, CloudFormation) • Design and integrate AWS services like EC2, S3, ECS, Batch, RDS, KMS into production environments • Ensure security, compliance, auditing, and reproducibility of scientific compute workloads • Support and optimize high-performance computing jobs spanning on-prem HPC clusters and AWS • Improve the reliability and throughput of analytical workflows processing petabytes of data • Collaborate with researchers to translate scientific needs into robust engineering solutions • Contribute to the design of microservices enabling scientific data exploration and visualization • Help build distributed systems supporting FAIR data principles • Partner with cross-functional teams to solve complex engineering problems end-to-end

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