Sr. Machine Learning Performance Engineer Job Listing at AMD in San Jose, CA (Job ID 47849-en-us)

Description

WHAT YOU DO AT AMD CHANGES EVERYTHING

We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world's most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives. 

AMD together we advance_

We seek a Sr. Machine Learning Performance Engineer to focus on ML Performance modeling, projection, and optimization for various ML workloads, and participate in hardware and software co-design. You will focus on the interaction between ML workloads and hardware architecture, including modeling workloads such as generative AI models on multiple HW configurations and summarize your recommendation.  Furthermore, you will be working with customers and business units on perf projection, analysis, and come up with novel solutions to satisfy customer needs.

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

As an ML Performance Engineer, you will analyze and explore recent ML models, understand their compute and memory requirements, and provide projection on various compute hardware for both inference and training. In addition, you will of profile and analyze various workloads on current hardware and come up with new ways to improve their performance. The ideal candidate will have strong experience with ML hardware architecture, software optimization and performance modeling.

• Performance modeling and analysis of ML training and inference workloads across a single and multiple accelerators. In addition of exploring various tradeoff and design decision.
• Participate in hardware-software co-design for future hardware optimization on various ML workloads.
• Communicate and present the results of the performance analysis and modeling to stakeholders and provide a concrete recommendation.
• Develop and improve our framework, tools and infrastructure for performance estimation, modeling and reporting.
• Cross team collaboration.

• Strong technical expertise and experience in performance analysis, projection, and ML hardware architecture.
• Experience with software optimization and performance modeling, including modeling workloads such as generative AI models on multiple HW configurations.
• Excellent written, verbal, and presentation skills.

• A PhD or master's degree, plus equivalent experience in computer science, electrical engineer, or a related field.

• #LI-MV1
• #LI-HYBRID
•  

At AMD, your base pay is one part of your total rewards package.  Your base pay will depend on where your skills, qualifications, experience, and location fit into the hiring range for the position. You may be eligible for incentives based upon your role such as either an annual bonus or sales incentive. Many AMD employees have the opportunity to own shares of AMD stock, as well as a discount when purchasing AMD stock if voluntarily participating in AMD's Employee Stock Purchase Plan. You'll also be eligible for competitive benefits described in more detail here.

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.