Power and Performance Validation Engineer

Graphcore•Austin, TX

About The Position

Graphcore is a leading innovator in Artificial Intelligence compute, developing hardware, software, and systems infrastructure to drive AI breakthroughs and widespread adoption. As part of the SoftWorks Group, Graphcore is committed to enabling Artificial Super Intelligence and making its benefits accessible to all. The company fosters a culture of continuous learning and innovation, with diverse teams of AI research specialists, silicon designers, software engineers, and systems architects. This role, reporting to senior leadership within Architecture and Validation, is for a Power and Performance Validation Lead. The position will drive the strategy and execution of validation for advanced AI compute silicon and systems. Key responsibilities include leading power, thermal, and performance validation activities in both pre-silicon and post-silicon environments to ensure products meet efficiency, reliability, and scalability expectations. The role demands strong technical expertise and cross-disciplinary collaboration to develop robust validation methodologies, scalable automation frameworks, and actionable performance insights.

Requirements

Strong experience in power and performance validation, silicon characterization, or system performance engineering.
Deep understanding of modern SoC architecture, including CPU, memory, interconnect, and high-speed I/O technologies.
Strong Linux systems knowledge and low-level performance analysis experience.
Strong Python programming skills for automation, orchestration, and data analysis.
Experience with benchmarking and profiling tools such as stress-ng, fio, perf, iperf, or equivalent technologies.
Experience debugging complex hardware and software interactions.
Ability to define structured validation methodologies, workload models, and test strategies.
Strong analytical skills with the ability to interpret large datasets and identify system bottlenecks.
Strong communication and cross-functional collaboration skills.
Ability to work independently on complex technical tasks while collaborating effectively across teams.

Nice To Haves

Experience with AI accelerator, GPU, or high-performance compute architectures.
Experience with pre-silicon modeling or simulation environments.
Knowledge of power management technologies and silicon characterization methodologies.
Programming experience in C/C++ for low-level system or benchmark development.
Familiarity with hardware instrumentation and telemetry systems.
Experience working with high core-count or large-scale compute systems.
Experience contributing to shared validation infrastructure and engineering best practices.

Responsibilities

Define and contribute validation strategies for power, thermal, and performance characterization of AI compute silicon and platforms.
Contribute to planning and execution of validation activities across multiple projects and milestones.
Develop comprehensive validation plans covering functional, stress, workload, and corner-case scenarios.
Work autonomously on post-silicon bring-up and characterization activities for power and performance validation.
Drive validation of CPU, memory, interconnect, and high-speed I/O subsystems under complex workload conditions.
Develop scalable automation frameworks, regression infrastructure, and reporting tools using Python.
Design and execute benchmark workloads, parameter sweeps, and performance experiments to identify optimization opportunities.
Collaborate with architecture, RTL, firmware, software, and systems teams to debug and resolve complex technical issues.
Define validation metrics, pass/fail criteria, and reporting methodologies to ensure repeatable and high-quality analysis.
Guide development of custom workload generators and micro-benchmarks where required.
Analyze power, thermal, and performance data to identify bottlenecks and recommend improvements.
Contribute to continuous improvement of validation processes, tooling, and engineering practices.
Communicate technical findings, project status, risks, and recommendations clearly to stakeholders and engineering leadership.
Support knowledge sharing and contribute to continuous improvement across the validation organization.