ML Compiler Engineer for Embedded Autonomy

Wayve; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Wayve in London is seeking a Staff ML Compiler Engineer to own the end-to-end ML compilation pipeline for embedded vehicle AI. You will design passes, extend lowering stages, and deliver deployable bundles for NVIDIA TensorRT and Qualcomm QNN targets.

You'll work across capture, decomposition, precision typing, and legalisation, ensuring accuracy and latency goals are met on diverse platforms. Strong Python and C++ experience is required, with MLIR/ONNX/TensorRT familiarity.

Qualifications

• Built or owned significant parts of an ML compilation or graph-lowering pipeline.
• Deep experience with quantisation in compilation — precision typing, PTQ integration, debugging accuracy loss from compiler transforms.
• Strong Python; comfortable building and testing compiler infrastructure in production codebases.
• Proficiency with at least one of: MLIR, ONNX, TensorRT, Qualcomm QNN, PyTorch graph capture/export.
• Experience with multi-target compilation or graph partitioning across hardware backends.
• Ability to reason about correctness and performance trade-offs at each compiler stage.

Responsibilities

• Own the ML compilation pipeline end-to-end on NVIDIA (TensorRT) and Qualcomm (QNN) targets.
• Design and implement compiler passes with accuracy and latency gates.
• Extend precision typing and graph-splitting logic for new architectures and SoCs.
• Partner with model and training teams on compilability.
• Build regression and benchmarking to validate changes across releases.
• Set technical direction and mentor on compiler design.

Skills

ML compilation

Graph lowering

Python

C++

Multi-target compilation

Quantisation

Tools

MLIR

ONNX

TensorRT

QNN

PyTorch

Job description

Wayve in London is seeking a Staff ML Compiler Engineer to own the end-to-end ML compilation pipeline for embedded vehicle AI. You will design passes, extend lowering stages, and deliver deployable bundles for NVIDIA TensorRT and Qualcomm QNN targets.

You'll work across capture, decomposition, precision typing, and legalisation, ensuring accuracy and latency goals are met on diverse platforms. Strong Python and C++ experience is required, with MLIR/ONNX/TensorRT familiarity.