Staff Software Engineer - ML Kernels & Runtime

At Graphcore, we’re building the future of AI compute. We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale. As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem. Graphcore is expanding its teams around the world to meet the vast and exciting AI opportunity.

Essential:

• Excellent programming and scripting skills using C++ and Python
• Understanding of processor architectures and profiling on Linux
• Experience testing numerical and performance-sensitive code
• Hands-on experience with reproducibility/determinism, tolerance design, and benchmarking
• Excellent written and oral communication skills, good work ethics, and a high sense of teamwork
• Commitment to producing quality work and being a team player

Desirable:

• Strong command of algorithmic performance including vectorisation, memory hierarchy, threading, and lock-free patterns
• Hands-on experience with at least one BLAS/DNN stack and ability to read/extend kernels
• Comfort with CPU micro-optimisations and numerical stability/trade-offs across FP32/FP16/BF16/FP8
• Experience integrating native code into PyTorch or similar (custom ops, extensions, dispatch keys)
• Knowledge of ABI/API stability and packaging on Linux systems (manylinux, wheels)

• Design and implement kernels for linear algebra and tensor operations (GEMM, batched GEMM, convolutions, reductions, elementwise and fused operations) in C++
• Own performance and correctness by adding microbenchmarks, regression tests, and numerics validation
• Profile and optimize for next generation AI hardware focusing on threading, cache locality, memory layout, and kernel launch efficiency
• Debug issues, resolve bugs, and improve the quality and functionality of the product
• Actively engage in and support Agile ways of working within the team
• Mentor colleagues within the team, sharing knowledge and providing guidance where appropriate