AI Power & Performance Leader, Embedded & Edge

AMD

Austin (TX)

On-site

USD 180,000 - 260,000

Full time

14 days+

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Job summary

AMD in Austin, TX is seeking a Senior Manager for AI Power & Performance to lead a global team delivering industry-leading AI performance, power efficiency, benchmarking, and optimization across Embedded and Edge AI platforms.

You will partner with architecture, software, silicon, product management, and marketing to ensure AMD solutions lead in AI workloads for Robotics, Physical AI, Automotive, Industrial, and Edge Computing.

Qualifications

  • Proven experience in software engineering, system performance, Edge physical AI, embedded platforms, or related domains.
  • Proven experience leading engineering teams and delivering complex technical programs.
  • Proven experience in Power and Power PnP activities Pre-Silicon and Post-Silicon & Correlation.
  • Strong understanding of AI/ML frameworks, inference runtimes, and performance optimization techniques.

Responsibilities

  • Define and execute AI power and performance strategy across AMD CPU, GPU, NPU, and heterogeneous computing platforms.
  • Establish benchmarking methodologies, performance KPIs, competitive analysis frameworks, and workload characterization processes.
  • Drive performance optimization across AI models, runtimes, kernels, frameworks, and system software stacks.
  • Identify bottlenecks and influence hardware, firmware, runtime, and software roadmap decisions through data-driven analysis.

Skills

Software engineering
System performance
AI/ML frameworks
Leadership
Embedded platforms

Education

Bachelor's/Master's in CS/CE/EE

Tools

ROCm
ONNX Runtime
MIGraphX

Job description

AMD in Austin, TX is seeking a Senior Manager for AI Power & Performance to lead a global team delivering industry-leading AI performance, power efficiency, benchmarking, and optimization across Embedded and Edge AI platforms.

You will partner with architecture, software, silicon, product management, and marketing to ensure AMD solutions lead in AI workloads for Robotics, Physical AI, Automotive, Industrial, and Edge Computing.

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