We use cookies. Find out more about it here. By continuing to browse this site you are agreeing to our use of cookies.
#alert
Back to search results

AI/ML Staff Systems Design Engineer -Power and Performance

Advanced Micro Devices, Inc.
USD $124,320.00/Yr.-USD $186,480.00/Yr.
United States, Texas, Austin
7171 Southwest Parkway (Show on map)
Nov 02, 2024


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_

THE ROLE:

We are looking for a dynamic, energetic performance & power characterization lead to join our growing team in AI Group. In this role, you will be responsible for characterizing and analyzing the performance and power consumption of AI models and systems mapped to NPU, providing insights that guide both hardware and software development. You will collaborate closely with engineering teams to ensure our AI solutions are both high-performing and energy-efficient. The team is dynamic and fast paced offering each of its members tremendous personal opportunity to grow their knowledge and skills in complex problem solving, product development and system engineering.

THE PERSON:

The Power and Performance Engineer will be responsible for overseeing the optimization of power consumption and performance across our AMD products. This role requires a deep understanding of hardware and software interactions, system architecture, and power management techniques.

KEY RESPONSIBILITIES:

  • Performance Characterization:
    • Pre-Silicon - Develop & simulate the test vectors for AI workload based power modelling.
    • Post- Silicon - Assess and document the performance characteristics of AI models on NPU. Identify performance bottlenecks and develop optimization strategies to enhance throughput and latency.
  • Power Characterization: Measure and analyze power consumption of AI workloads, identifying key factors affecting energy efficiency. Develop and implement techniques to optimize power usage while maintaining high performance.
  • Pre to Post Silicon correlation analysis: Methodology Development for platform power measurement co-relation analysis & debug.
  • Benchmarking: Develop and maintain performance and power benchmarks for AI workloads. Design experiments and collect data to evaluate and compare the efficiency of different hardware and software configurations.
  • Data Analysis: Analyze performance and power data to derive actionable insights. Utilize statistical methods and visualization tools to present findings and recommendations to stakeholders.
  • Collaboration: Work with hardware engineers, software developers, and data scientists to align characterization efforts with system requirements and constraints. Provide feedback and recommendations to influence design decisions.
  • Tool Development: Create and maintain tools and frameworks for automated performance and power characterization. Enhance existing tools to support new AI models and hardware platforms.
  • Documentation and Reporting: Prepare comprehensive reports and documentation detailing characterization results, methodologies, and recommendations. Communicate findings effectively to both technical and non-technical audiences.
  • Continuous Improvement: Stay updated with the latest advancements in AI, hardware, and power management technologies. Identify opportunities for process improvements and implement best practices in characterization.

PREFERRED EXPERIENCE:

    • Proficiency in performance and power profiling tools and techniques.
    • Strong understanding of AI model architectures and their computational requirements.
    • Experience with hardware performance counters and power measurement tools.
    • Familiarity with AI frameworks (e.g., TensorFlow, PyTorch) and their integration with hardware.
    • Proficiency in programming and scripting languages (e.g., Python, C/C++).
    • Experience with Client PC power modelling is preferred.
    • Strong analytical and problem-solving skills.
    • Excellent communication skills, with the ability to convey complex technical information clearly.
    • Ability to work effectively in a collaborative, fast-paced environment.
    • Detail-oriented with a strong commitment to accuracy and quality.

ACADEMIC CREDENTIALS:

  • Bachelors or Masters degree in electrical or computer engineering

#LI-RF1

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.

Applied = 0

(web-69c66cf95d-nlr4c)