Senior Machine Learning Engineer, AI Performance

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About the role

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our ML Optimisation Team (AI Performance) We're a high-ownership team responsible for delivering production-ready model releases as Wayve's OEM engagements and release cadence accelerate. We're applied and delivery-focused: we take models from "works in training" to "meets product constraints," working closely with downstream inference and performance specialists to get models ready for on-vehicle deployment. 🧠 Your day-to-day • Owning end-to-end delivery of model releases, from initial requirements through training, evaluation, iteration and deployment readiness • Training and iterating on PyTorch models with a hypothesis-driven approach, running ablations against clear evaluation criteria • Debugging model performance: identifying regressions, root-causing issues and proposing fixes • Collaborating with adjacent ML and performance engineering teams to hand off models, define bottlenecks and align on optimisation priorities • Communicating with stakeholders on delivery timelines, trade-offs and readiness criteria 🧩 What you'll be working on • Getting models to meet tight runtime constraints on-vehicle as model capability grows • Applying practical optimisation techniques such as quantisation, distillation and low-rank methods, where the trade-offs make sense • Shaping the handoff between training, evaluation and deployment, so models ship quickly and reliably • Working at multiple levels of abstraction, from high-level model behaviour down to runtime and latency implications 🙌 You should apply if • You have proven experience improving performance in production systems with tight constraints (latency, memory, bandwidth, power/thermal or cost) • You have strong hands-on experience training and iterating on deep learning models in PyTorch, beyond high-level tooling • You're proficient with at least one relevant stack or toolchain (e.g. TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL) and can pick up adjacent frameworks quickly • You're comfortable moving between high-level model behaviour and low-level kernel/runtime execution • You're familiar with model optimisation concepts such as quantisation and/or distillation (hands-on is a strong signal, but solid fundamentals are enough) • You have strong engineering fundamentals and collaboration skills • Bonus: experience with models under tight latency/efficiency constraints (edge, embedded, real-time), exposure to ML systems from training through to deployment handoff, and embedded/edge deployment including benchmarking on real devices 🌱 Not ticking every box? That's totally okay! If you're passionate about autonomy and keen to learn, we encourage you to apply even if you don't meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicle

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