AI Performance Engineer

About us

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute.

It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.

As part of the Soft Bank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.

Graphcore’s AI/ML training and inference infrastructure is rapidly scaling to meet the growing demands of AI workloads across mobile, edge, and datacenter environments. This role focuses on optimizing performance across ARM-based architectures and large-scale distributed systems, ensuring efficiency, scalability, and reliability across the full hardware-software stack.

The System Engineering Performance team architects and optimizes high-performance infrastructure for large-scale datacenter deployments. The team works across hardware, software, networking, and system architecture to deliver cutting-edge AI solutions and ensure optimal system performance at scale.

• Analyze ML models’ compute and memory requirements using roofline analysis and simulations
• Collaborate across hardware and software teams to optimize large-scale AI workloads
• Benchmark, monitor, and troubleshoot system performance across distributed systems
• Optimize communication stacks including MPI, NCCL, UCX, RDMA, and networking fabrics
• Profile and optimize AI workloads, focusing on performance bottlenecks
• Develop high-quality, ARM-compatible code and documentation

Essential:

• BS/MS in Computer Science, Electrical Engineering, or related field
• Experience with distributed systems and communication libraries (MPI, NCCL, UCX, libfabric)
• Strong programming skills in C++ and Python
• Experience profiling and optimizing HPC or AI/ML workloads
• Familiarity with ML benchmarks such as MLPerf

Desirable:

• Experience with GPUs or accelerated computing architectures
• Knowledge of HPC networking and interconnect technologies (Infini Band, RoCE)
• Familiarity with ML frameworks such as PyTorch or Tensor Flow
• Understanding of ARM architectures and tool chains
• Strong debugging, profiling, and performance optimization skills