SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Graphcore is seeking a Staff AI Performance Engineer to lead performance optimization work across large-scale AI infrastructure. In this role, you will analyze and optimize AI training and inference workloads across distributed systems, connecting compute, memory, networking, and software behavior to drive efficiency and scalability.
You will lead complex performance workstreams spanning hardware and software, identifying bottlenecks, validating improvements, and translating performance evidence into actionable engineering decisions. Your responsibilities include building benchmarks, profiling demanding workloads, and developing C++ and Python tools for analysis and optimization. Your recommendations will help teams improve efficiency, scalability, and reliability across data center-scale deployments.
The System Engineering Performance team architects, evaluates, and optimizes high-performance infrastructure for large-scale data center deployments. Work is driven by evidence, ownership, and clear technical judgment. You will lead investigations, coordinate across teams, and validate impact with reliable performance data. The team values engineers who think strategically, act decisively, take responsibility, and lead beyond their immediate scope.
This role offers broad technical scope across AI infrastructure at data center scale and is based in Milpitas, California.
QUALIFICATIONS:
- Strong experience profiling and optimizing AI, 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 lead technically complex work and coordinate improvements across teams
- Familiarity with MLPerf, accelerated architectures, ML frameworks, or high-performance interconnects
The company welcomes transferable skills and diverse experiences, including engineers returning to the profession after a career break.
