Sr. Software Development Engineer (GPU Machine Learning Performance)

Advanced Micro Devices, Inc•Austin, TX

4d•Hybrid

About The Position

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. THE ROLE: AMD’s Advanced Technology Group is an entrepreneurial research and development team to build AMD's future advanced platforms & products. Our teams work closely with outside customers and internal teams to develop hardware, software, and systems solutions into next generation computing platforms. As part of AMD Advanced Technology Group, you will have the opportunity to be part of a winning team that will collaborate with internal teams & customers, explore new platform technologies, and lead the development of best-in-class hardware, software, and systems technologies that our customers will use for real-world problems. In this role, you will join our team working on AMD’s next generation GPU edge inference solutions, ensuring machine learning solutions hit performance goals through GPU performance analysis, profiling, workload optimization and other duties to unlock powerful experiences for end-users with AMD devices. THE PERSON: AMD is looking for a seasoned Principle Staff Member, to join our Advanced Technology Group GPU based Machine Learning development to work on AMD’s long-term Graphics and Machine Learning solutions, which will have significant impact for AMD, and the future of our products. Our group works on forward-looking projects and novel system concepts that significantly impact AMD’s future product portfolio. Your role will contribute to shaping AMD’s forward-looking hardware and software technologies.

Requirements

Strong experience in high‑performance GPU computing, inference optimization, or systems‑level performance engineering.
Deep understanding of machine learning architectures and large‑scale inference behavior.
Demonstrated success in building, optimizing, Machine Learning kernels, workloads, models for performance and power.
Proficiency in GPU profiling tools, memory analysis, and performance tracing (e.g., HIP/CUDA profiling tools, custom instrumentation).
Ability to reason about compute/memory tradeoffs, kernel scheduling, KV‑cache patterns, and parallel execution models.
Hands‑on experience with GPU kernels, inference runtimes, or compiler‑adjacent optimizations.
Demonstrated track record of delivering measurable performance improvements in complex systems.
Ability to drive technical direction in ambiguous or rapidly evolving problem spaces.
Track record in using AI tooling to accelerate engineering work.
Strong collaboration and communication skills.
Master / Bachelor of Computer Systems, Computer Science, Software Engineering, or similar degree.

Responsibilities

Work within a team on Machine Learning SW and Workload development in pre and post-silicon phases of product development enabling next-generation of ML workloads in functional and performance attainment.
Profile end‑to‑end inference pipelines to identify performance bottlenecks spanning kernels, scheduling, memory behavior, and model execution logic.
Optimize execution paths for transformer models, including attention computation, KV‑cache behavior, and efficient memory layouts.
Develop and refine strategies for batching, parallelism, quantization, and distributed execution across single‑node and multi‑node environments.
Collaborate with compiler, kernel and user mode driver teams, and hardware architecture teams to improve system‑level efficiency and remove scaling blockers.
Build performance models, benchmarking suites, and automated evaluation tools to quantify gains and track regressions.
Shape best practices for profiling, debugging, and performance tuning across engineering teams.
Communicate results, insights, and recommendations clearly to both technical and non‑technical partners.
Contribute to AMD’s next-generation GPU Machine Learning technologies.