IT & Software

Software Performance Engineer

ARM

Cambridge · Cambridgeshire · United Kingdom

Overview

In Arm’s Machine Learning group, you will analyze ML/AI performance on next-generation Arm IP and feed insights back to hardware and software teams. You’ll produce technical summaries for varied audiences and share knowledge across the company. The role blends hands-on performance work with cross‑functional collaboration to shape Arm ML software, systems, and IP. This is a chance to impact how ML workloads run on Arm devices at scale and contribute to pioneering AI technology.

Pay / Benefits
  • sponsorship for skilled worker
  • accommodations during recruitment process
  • hybrid working arrangements
  • equal opportunities
  • supportive and diverse team
  • international collaboration (Arm ecosystem)
Responsibilities
  • Run and analyse ML/AI performance on next-generation Arm IP and provide feedback to software and hardware teams
  • Produce technical summaries from detailed data analysis for diverse audiences
  • Collaborate with cross-functional teams across Arm to share findings and learn
  • Contextualize results for internal and external stakeholders to influence development directions
  • Support knowledge transfer within the company by sharing insights and best practices
Key requirements
  • Demonstrable experience in pre-silicon or post-silicon performance analysis
  • Hands-on experience running workloads on Linux or Android devices, development boards, or pre/post-silicon environments
  • Experience using or developing automation harnesses for repeatable workload execution, data collection, profiling, and analysis
  • Strong scripting and data-analysis skills (e.g., Python, shell scripting)
  • Experience with profiling tools, system traces or telemetry
  • Experience with pre-silicon platforms such as FPGA
  • Passion for analysis and improvement
  • Ability to work both in teams and independently
  • High initiative and a confident problem solver
  • Performance analysis
  • Linux/Android workload execution
  • Automation harnesses for workload execution and data collection

Reference: WJ-747_31177344

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