Senior Machine Learning Engineer, AI Performance

AI Startups UK; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Wayve is seeking a Senior Machine Learning Engineer to own end-to-end model releases for production systems with tight latency and resource constraints. You will drive development from training to on-vehicle deployment, collaborating with performance and inference teams to meet product needs.

You will iteratively improve PyTorch models, apply optimization techniques like quantisation and distillation, and ensure deployments align with real-world constraints.

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 high-level tooling).
• Proficiency with stack/toolchain such as TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL and ability to learn adjacent frameworks quickly.
• Comfort operating at multiple levels of abstraction—from high-level model behavior to low-level kernel/runtime execution.
• Familiarity with model optimisation concepts such as quantisation and distillation (hands-on is preferred).
• Ability to reason across model behavior and runtime/latency implications.

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) and understand trade-offs and when to use them.
• Collaborate cross‑functionally with adjacent ML and performance engineering teams to hand off models and align on bottlenecks and priorities.
• Communicate clearly with stakeholders to align on delivery timelines and readiness criteria.

Skills

Production performance

PyTorch

Model optimisation

Quantisation

Distillation

Tools

PyTorch

TensorRT

CUDA

Triton

OpenCL

QNN

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 models, including benchmarking on real devices and handling system‑level constraints.

#LI-HH1

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