Applied AI & GPU Software Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. APPLIED AI & GPU SOFTWARE ENGINEER THE ROLE: AMD is seeking a Software Engineer to join the Software Ecosystem Enablement team, focused on deploying and optimizing machine learning inference in commercial applications in content creation and CAE spaces. This role targets performance, scalability, and memory efficiency across heterogeneous platforms (GPU, NPU, CPU), enabling next‑generation ML‑driven features in real-world applications. THE PERSON: The ideal candidate is a strong systems‑focused engineer with deep GPU fundamentals and an interest in applying modern ML techniques to production software. A successful candidate will be comfortable navigating ambiguity, learning new technologies, and working across multiple technical domains. KEY RESPONSIBILITIES: Investigate and prototype hybrid ML systems for graphics, simulation, and media-generation pipelines Integrate existing ML models and inference pipelines into commercial software systems Design efficient workload scheduling and distribution across heterogeneous resources Profile workloads across GPU, NPU, and CPU to identify bottlenecks and optimize performance Evaluate runtimes, execution providers, and deployment strategies for modern hardware architectures Collaborate with domain experts and existing GPU engineering teams PREFERRED EXPERIENCE: 10+ years of professional software development experience Solid programming fundamentals in C/C++ Experience developing or contributing to GPU-accelerated applications Solid understanding of GPU programming fundamentals Debugging experience with GPU kernels or performance‑critical code Experience with modern ML frameworks and inference systems Experience with denoising, neural rendering, or ML simulation is an asset Experience with content creation apps, CAD/CAE tools, or HPC pipeline is an asset ACADEMIC CREDENTIALS: Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent #LI-SF1 #LI-Remote Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

APPLIED AI & GPU SOFTWARE ENGINEER THE ROLE: AMD is seeking a Software Engineer to join the Software Ecosystem Enablement team, focused on deploying and optimizing machine learning inference in commercial applications in content creation and CAE spaces. This role targets performance, scalability, and memory efficiency across heterogeneous platforms (GPU, NPU, CPU), enabling next‑generation ML‑driven features in real-world applications. THE PERSON: The ideal candidate is a strong systems‑focused engineer with deep GPU fundamentals and an interest in applying modern ML techniques to production software. A successful candidate will be comfortable navigating ambiguity, learning new technologies, and working across multiple technical domains. KEY RESPONSIBILITIES: Investigate and prototype hybrid ML systems for graphics, simulation, and media-generation pipelines Integrate existing ML models and inference pipelines into commercial software systems Design efficient workload scheduling and distribution across heterogeneous resources Profile workloads across GPU, NPU, and CPU to identify bottlenecks and optimize performance Evaluate runtimes, execution providers, and deployment strategies for modern hardware architectures Collaborate with domain experts and existing GPU engineering teams PREFERRED EXPERIENCE: 10+ years of professional software development experience Solid programming fundamentals in C/C++ Experience developing or contributing to GPU-accelerated applications Solid understanding of GPU programming fundamentals Debugging experience with GPU kernels or performance‑critical code Experience with modern ML frameworks and inference systems Experience with denoising, neural rendering, or ML simulation is an asset Experience with content creation apps, CAD/CAE tools, or HPC pipeline is an asset ACADEMIC CREDENTIALS: Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent #LI-SF1 #LI-Remote