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Performance Analysis Engineer

Austin, TX — Full-time

Join the most cutting-edge and well-funded hardware startup in Silicon Valley as a Workloads Analyst Engineer. Our mission is to reimagine silicon and create computing platforms that will transform the industry. You will have the opportunity to work with some of the most talented and passionate engineers in the world to create designs that push the envelope on performance, energy efficiency and scalability. We offer a fun, creative and flexible work environment, with a shared vision to build products to change the world.

As a Performance Analysis Engineer, you will be working on server-class workload characterization, tracing and competitive analysis.

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Minimum qualifications

  • BS degree in Electrical / Computer Eng with 2 years of relevant experience
  • Good understanding of computer architecture and OS fundamentals
  • Strong scripting skills -- Bash / Perl / Python
  • Proficiency in C / C++ / x86, ARM assembly
  • Experience with CPU performance models

Preferred qualifications

  • MS degree in Electrical / Computer Eng with 5 years of relevant experience
  • Experience with hardware performance counter and binary instrumentation tools
  • Experience with characterizing server-class workloads such as Memcached, NGINX, SpecJBB 
  • Experience in writing micro-benchmarks to measure SoC speeds-and-feeds
  • Prior experience in tracing workloads to drive performance prediction models
  • Experience with CPU performance simulators / analytical models
  • Experience in Linux kernel internals / system programming
  • Knowledge of the competitive landscape, feature specifications and industry trends in the server and client domain

Roles and Responsibilities

  • Collecting power-performance data for various workloads / benchmarks
  • Conducting detailed workload characterization
  • Writing micro-benchmarks to understand performance characteristics of a SoC
  • Collect traces to feed the performance simulation model
  • Build competitive performance prediction models
  • Present the power-performance data in a concise manner
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