We’re looking for a Senior Machine Learning Engineer to join a high-ownership team responsible for delivering production-ready model releases as our OEM engagements and release cadence accelerate. This is an applied, delivery-focused MLE role—ideal for engineers who love shipping real systems and iterating quickly.
🏢 About Wayve
Wayve is an AI company focused on developing autonomous driving technology.
🎯 The Role
You’ll work on taking models from “works in training” to “meets product constraints,” partnering closely with teams downstream (e.g., inference/performance specialists) to ensure models are ready for deployment on-vehicle. As model capability grows, you’ll help keep the system within tight runtime constraints using a practical model optimisation techniques (e.g., quantisation, distillation, low-rank methods) where appropriate.
✅ Key Responsibilities
Own end-to-end delivery of model releases, from initial requirements through training, evaluation, iteration, and final readiness for deployment.
Train and iterate on PyTorch models with a strong experimental approach (hypothesis-driven iteration, ablations, clear evaluation criteria).
Debug and improve model performance using strong analytical skills—identifying regressions, root-causing issues, and proposing fixes.
Apply optimisation techniques (e.g., quantisation and distillation where beneficial), understanding trade-offs and when methods are appropriate.
Collaborate cross-functionally with adjacent ML and performance engineering teams to hand off models, define bottlenecks, and align on optimisation priorities.
Communicate clearly with stakeholders to align on delivery timelines, trade-offs, and readiness criteria.
📌 Required Qualifications
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.
⭐ Desirable Experience
Experience working on models that must meet tight latency / efficiency constraints (edge, embedded, real-time, or similarly constrained production settings).
Exposure to ML systems spanning training → evaluation → deployment handoff (even if you’re not writing kernels day-to-day).
Exposure to embedded or edge deployment of ML models, including benchmarking on real devices and handling system-level constraints.
