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Graphcore

Delivers IPU-powered AI cloud services

Senior AI Performance Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeAustin, TX, USANo salary listedHybrid

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 MPI, NCCL, UCX, or libfabric.
  • Strong C++ and Python skills, including building reliable tools or performance-sensitive software.
  • Deep understanding of compute, memory, and communication behavior in large-scale systems.
  • Ability to own complex technical work and coordinate improvements across teams.
  • Familiarity with MLPerf, accelerated architectures, machine learning frameworks, or high-performance interconnects.

Responsibilities

  • Analyze and optimize artificial intelligence training and inference workloads across large-scale distributed systems.
  • Own complex performance investigations from problem definition through validation.
  • Translate profiling, benchmarking, and modeling results into improvements that engineers can implement.
  • Design benchmarks, investigate system bottlenecks, and optimize distributed communication software across technologies such as MPI, NCCL, UCX, and RDMA.
  • Work across hardware, software, networking, and system architecture to improve efficient and reliable artificial intelligence systems for large-scale deployment.
  • Define investigations, coordinate across teams, and validate impact with reliable performance data.

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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