Senior Machine Learning Engineer, AI Performance

Proven experience improving performance in production systems with tight constraints (latency, memory, bandwidth, power/thermal, or cost).

Strong hands-on experience training and iterating on deep learning models in PyTorch (not just using high-level tooling).

Strong proficiency with at least one relevant stack/toolchain (e.g. TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL) and confidence learning adjacent frameworks quickly.

Comfort operating at multiple levels of abstraction — from high-level model behaviour down to low-level kernel/runtime execution.

Familiarity with model optimisation concepts such as quantisation and/or distillation (hands-on is a strong signal, but not a strict requirement if the fundamentals are solid).

Ability to reason across multiple levels of abstraction—from high-level model behaviour down to practical runtime/latency implications.

Strong engineering fundamentals and collaboration skills.