AI Applications Engineer

AMD develops advanced computing technologies for healthcare, scientific discovery, artificial intelligence, and other applications. The AI Applications Engineer will develop AI-assisted workflows for FPGA and SoC reference designs, optimize AI models and inference deployments across hardware platforms, and contribute to AI infrastructure security initiatives. The role also involves research, prototyping, technical content creation, benchmarking, and collaboration with engineering, product, field, and customer teams.

What you'll do:

• Develop and utilize AI-assisted workflows to accelerate the creation, validation, and optimization of FPGA and SoC reference designs
• Apply AI tools and techniques to improve engineering productivity throughout design and verification processes
• Fine-tune, evaluate, and optimize open-source AI models for embedded, edge, and security-focused applications
• Implement and optimize AI inference solutions on FPGA SoCs, NPUs, and GPUs, balancing performance, latency, power consumption, and resource utilization
• Research and prototype innovative solutions for securing AI infrastructure deployments in enterprise environments
• Contribute to industry and open-source communities focused on AI, embedded computing, and security technologies
• Generate technical content, benchmarks, proof-of-concepts, and recommendations that support product strategy and long-term technology roadmaps
• Collaborate with engineering, product management, field teams, and customers through technical presentations and knowledge-sharing initiatives

What they're looking for:

• * Bachelors or Masters degree in Electrical Engineering, Mathematics, Computer Science, Engineering, or an equivalent
• * San Jose, CA with a hybrid work environment
• * Approximately **10% travel** for industry conferences, customer engagements, and cross-functional collaboration activities
• This role is not eligible for visa sponsorship
• * Experience in AI/ML engineering, applied research, software productivity initiatives, or related technical fields
• * Hands-on experience with training, fine-tuning, quantization, and evaluation of open-source AI models
• * Strong understanding of machine learning fundamentals, neural networks, optimization techniques, probability, and statistics
• * Proficiency in Python or C/C++, Linux development environments, and Git-based version control
• * Familiarity with FPGA design methodologies, RTL development (Verilog/VHDL), High-Level Synthesis (HLS), or embedded systems development
• * Exposure to edge AI deployment, AI model optimization, AI infrastructure security, or hardware-accelerated computing is a plus
• * Interest in learning FPGA/SoC architectures, AI stack optimization, and hardware-level security technologies

Benefits:

• San Jose, CA with a hybrid work environment.