Senior Director, ML Workload Performance

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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 :

We are seeking an exceptional Senior Director of Engineering to lead our Machine Learning workload performance and optimization team across various frameworks and model architectures. This is a highly visible role with large scope and impact.

As Senior Director of ML workload performance, you will lead and build a team of experts in ML, optimizations and system‑level software focusing on getting the best performance on multiple generations of AMD GPUs with a wide range of models including the latest state‑of‑the‑art AI models. You will set strategy and roadmap for general optimization and accelerating supporting new models.

Furthermore, you will be working with customers and business units on performance projection, analysis and come up with novel solutions to satisfy partner needs. You will also participate in standard performance benchmarks.

If you are passionate about performance optimization, getting the best out of the hardware, and shaping the future of AI acceleration, then this role is for you.

THE PERSON :

The ideal candidate will have a proven track record of building and leading a world‑class performance engineering team. Experience with ML hardware architecture, software optimization, performance modelling and the latest trends in inference and training. Experience in mapping model architecture to low‑level software, hardware and understanding the impact of each layer of the stack to model performance.

Strong knowledge in latest generative model architecture, especially SoTA models, distributed inference and deployment at scale is crucial.

KEY RESPONSIBILITIES :

• Set strategy and roadmap for AMD model optimization.
• Lead, mentor and scale a world‑class high performance team focused on ML workload optimization.
• Performance tuning, profiling and analysis of large‑scale models for LLM, diffusion, multimodal, RecSys and generative AI, single node and distributed. In addition to exploring various trade‑offs and design decisions.
• Partner with the business, customer solution, and various engineering organizations to deliver the best performance across AMD GPUs.
• Participate in hardware‑software co‑design for future hardware optimization on various ML workloads.
• Develop and improve framework, tools and infrastructure for performance estimation, modelling and reporting.
• Communicate and present the results of the performance analysis and modelling to customers, stakeholders, and senior leadership, and provide a concrete recommendation.
• Cross‑team collaboration and working across the organization to identify opportunities and develop strategies.

PREFERRED EXPERIENCE :

ACADEMIC CREDENTIALS :

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.

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