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Graphcore

Delivers IPU-powered AI cloud services

Staff AI Performance Engineer

AI & Machine LearningConfirmed live in the last 24 hours
Full-timeMilpitas, CA, USANo salary listedIn-person

About the Job

Requirements

  • Strong experience profiling and optimizing artificial intelligence, machine learning, or high-performance computing workloads.
  • Experience with distributed systems and communication libraries such as Message Passing Interface, NVIDIA Collective Communications Library, Unified Communication X, or libfabric.
  • Strong C++ and Python skills, including the ability to develop reliable tools or performance-sensitive software.
  • Deep understanding of compute, memory, and communication behavior in large-scale systems.
  • Ability to lead technically complex work and coordinate improvements across teams.

Responsibilities

  • Analyze and optimize artificial intelligence training and inference workloads across distributed systems.
  • Lead complex performance workstreams across hardware and software.
  • Identify bottlenecks, validate improvements, and translate performance evidence into practical engineering decisions.
  • Build benchmarks, profile demanding workloads, and develop C++ and Python tools for analysis and optimization.
  • Make recommendations to help teams improve efficiency, scalability, and reliability.
  • Lead investigations, coordinate across teams, and validate impact with reliable performance data.

Desired Qualifications

  • Familiarity with MLPerf, accelerated architectures, machine learning frameworks, or high-performance interconnects.

About the Company

Graphcore

Delivers IPU-powered AI cloud services

Graphcore.ai focuses on building and delivering Intelligence Processing Units (IPUs) and related AI services for enterprises. It provides IPU-powered cloud services, pre-trained AI models, optimized inference engines, and APIs on a subscription basis with on-demand pricing and free tiers through cloud partners. The IPUs accelerate AI workloads and enable scalable, cost-efficient AI deployment in enterprise applications. The company differentiates itself through specialized IPU hardware and optimized software stack, plus partnerships with cloud providers to reach customers, rather than relying on generic hardware. The goal is to help businesses scale AI-powered products and operations efficiently by offering flexible, scalable compute and ready-to-use AI components.

Company size
501-1,000
Company stage
Growth Equity (Non-Venture Capital)
Total funding
$1.1B
Headquarters
Bristol, United Kingdom
Founded
2016

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