AI Performance Engineer

Requirements

• BS/MS in Computer Science, Electrical Engineering, or related field
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• Experience with distributed systems and communication libraries (MPI, NCCL, UCX, libfabric)
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• Strong programming skills in C++ and Python
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• Experience profiling and optimizing HPC or AI/ML workloads
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• Familiarity with ML benchmarks such as MLPerf
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• (Desirable) Experience with GPUs or accelerated computing architectures
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• (Desirable) Knowledge of HPC networking and interconnect technologies (Infini Band, RoCE)
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• (Desirable) Familiarity with ML frameworks such as PyTorch or Tensor Flow
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• (Desirable) Understanding of ARM architectures and tool chains
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• (Desirable) Strong debugging, profiling, and performance optimization skills

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