Senior Machine Learning Engineer, AI Performance

Overview

In this role, you own end-to-end delivery of production-ready model releases for Wayve’s autonomous-driving platform, from requirements through training, evaluation and deployment readiness. You work closely with inference and performance teams to meet tight on-vehicle constraints and drive optimisation. You apply practical techniques to boost latency, memory, and power efficiency while ensuring reliable handoffs between training, evaluation and deployment. You’ll influence cross-functional stakeholders and shape how models ship at scale, delivering impact in real-world driving scenarios.

Pay / Benefits

• salaries benchmarked against market
• meaningful equity
• relocation support and visa sponsorship
• hybrid working with core hours
• learning and development budgets
• comprehensive benefits including health insurance, dental, maternity/paternity leave, retirement/pension, wellbeing programs

Responsibilities

• Own end-to-end model release delivery from requirements to deployment readiness
• Train and iterate PyTorch models using hypothesis-driven ablations
• Debug performance regressions and identify root causes with fixes
• Collaborate with adjacent ML and performance engineering teams to align on bottlenecks and priorities
• Communicate delivery timelines, trade-offs, and readiness criteria to stakeholders
• Ensure models meet strict on-vehicle runtime constraints as capabilities evolve
• Apply optimisation techniques (quantisation, distillation, low-rank methods) where appropriate
• Shape handoff between training, evaluation and deployment for fast, reliable shipping
• Address performance across high-level behavior down to runtime latency implications

Key requirements

• Proven experience improving performance in production systems with tight constraints (latency, memory, power/cost)
• Hands-on experience training and iterating on deep learning models in PyTorch
• Proficiency with at least one of TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL and the ability to learn related stacks quickly
• Comfort moving between high-level model behavior and low-level kernel/runtime execution
• Familiarity with model optimisation concepts such as quantisation and/or distillation (hands-on preferred)
• Strong engineering fundamentals and collaboration skills

• collaboration
• communication
• problem-solving
• PyTorch
• model training and iteration
• quantisation

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