Performance Engineer - Inference

We are looking for a Performance Engineer, Inference, to understand and improve the systems that serve our foundation models. Inference is a tightly coupled system spanning model execution, serving runtimes, distributed systems, accelerators, scheduling, memory, and reliability. You will measure the system end-to-end, identify the highest-leverage performance gaps, and work across teams to close them while preserving correctness.

Responsibilities:

• Run cross-layer performance investigations across throughput, latency, memory efficiency, reliability, and cost.
• Build profiling, benchmarking, and observability tools that make inference performance measurable and explainable.
• Identify bottlenecks across model servers, batching and scheduling, distributed execution, memory systems, and accelerators.
• Partner with model, platform, and infrastructure teams to prioritise and land high-impact optimisations.
• Validate that performance improvements preserve model quality and numerical correctness.

Requirements:

• Hands-on experience profiling and optimising ML systems or other performance-critical production systems.
• Production experience with at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, or an equivalent serving runtime.
• Experience serving or operating large models across accelerator-backed infrastructure, including multi-GPU or multi-accelerator systems.
• Strong Python skills and the ability to read, instrument, and modify large production codebases.
• Solid understanding of transformer inference, distributed systems, latency/throughput trade-offs, and accelerator performance fundamentals.

Preferred Qualifications:

• Experience with large-scale or multi-node inference, including tensor, pipeline, data, or expert parallelism.
• Experience with GPUs, TPUs, NPUs, or other ML accelerators and associated profiling tools.
• Experience with quantisation, low-precision inference, KV-cache optimisation, speculative decoding, or long-context serving.
• Experience contributing to or modifying inference runtimes, kernels, compilers, or distributed serving components.
• Experience optimising inference for constrained or on-device environments.