Senior Machine Learning Engineer, AI Performance

Overview

As a Senior Machine Learning Engineer at Wayve, you will own the end-to-end delivery of production-ready model releases for OEM engagements, ensuring models meet product constraints and deploy on-vehicle. You’ll train and iterate PyTorch models with a rigorous experimental approach, collaborating with inference and performance teams to optimize runtime. You’ll apply practical model optimization techniques to maintain tight latency and resource usage, shaping systems that enable scalable autonomous driving.

Responsibilities

• Own end-to-end delivery of model releases from requirements to deployment readiness
• Train and iterate on PyTorch models with hypothesis-driven evaluation
• Debug and improve model performance by identifying regressions and root-causes
• Apply optimisation techniques (quantisation, distillation) and assess trade-offs
• Collaborate cross-functionally with ML and performance engineering teams to hand off models and set priorities
• Communicate delivery timelines, trade-offs, and readiness criteria to stakeholders

Key requirements

• Proven experience improving performance in production systems with tight constraints (latency, memory, bandwidth, power/thermal, or cost)
• Hands-on experience training and iterating on deep learning models in PyTorch
• Proficiency with at least one stack/toolchain (e.g. TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL) and ability to learn adjacent frameworks quickly
• Comfort operating across abstraction levels from high-level model behavior to kernel/runtime execution
• Familiarity with model optimisation concepts such as quantisation and/or distillation
• Ability to reason across multiple abstraction levels and understand runtime/latency implications
• Strong engineering fundamentals and collaboration skills

• Strong collaboration
• Analytical mindset
• Clear communication with stakeholders
• PyTorch
• TensorRT
• CUDA

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