Senior Machine Learning Engineer, AI Performance

EngineersOfAI; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Wayve is seeking a Senior Machine Learning Engineer to join a high-ownership team delivering production-ready model releases for OEM engagements. This delivery-focused role centers on taking models from training to deployment, working with downstream specialists to ensure on-vehicle viability, and applying practical optimization techniques to meet runtime constraints.

You will ship real systems, iterate quickly, and collaborate across ML and performance engineering teams to balance trade-offs

Qualifications

• Proven experience improving performance in production systems with tight constraints.
• Strong hands-on experience training and iterating on deep learning models in PyTorch.
• Proficiency with TensorRT/CUDA/QNN/Triton/OpenCL and ability to learn adjacent frameworks quickly.
• Comfort operating from high-level model behaviour to low-level kernel/runtime execution.
• Familiarity with model optimisation concepts such as quantisation and distillation.
• Ability to reason across multiple levels of abstraction and communicate trade-offs clearly.
• Strong engineering fundamentals and collaboration skills.

Responsibilities

• Own end-to-end delivery of model releases from requirements through training, evaluation and readiness for deployment.
• Train and iterate on PyTorch models with an experimental approach.
• Debug and improve model performance focusing on latency, memory and bandwidth constraints.
• Apply optimisation techniques such as quantisation and distillation where beneficial.
• Collaborate with ML and performance engineering teams to hand off models and align on priorities.
• Communicate delivery timelines, trade-offs, and readiness criteria to stakeholders.

Skills

Performance optimization

PyTorch

Quantisation

Distillation

Low-level optimization

Collaboration

End-to-end delivery

Tools

TensorRT

CUDA

QNN

Triton

OpenCL

Job description

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

Make Wayve the experience that defines your career!

The role

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.

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.

About you

In order to set you up for success in this role at Wayve, we’re looking for the following skills and experience:

Essential

• 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 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 mod