Machine learning engineer in autonomous driving

Wayve; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid working model

Market-rate salary benchmarking

Relocation support and visa where/app

Comprehensive benefits includinghealth

Dental

Enhanced maternity and paternity leave

Retirement or pension

Wellbeing and therapy programs

Learning and development budgets

Job summary

Wayve is building embodied intelligence for safe autonomous driving. The Core Model Safety team leads large-scale data enrichment, extraction, and model evaluation, collaborating with Pre/Midtraining, reward modelling, and simulation groups.

You will design workflows for hard/rare event mining and robust model introspection, shaping ML ops across teams. You will drive leadership in data-driven development and deployable ML systems, contributing to the scalable, researchers-to-engineers pipeline

Qualifications

• Hands-on experience with ML systems deployed in the real world.
• Proficiency in Python and PyTorch with solid software engineering practices.
• Experience with distributed computing environments and large-scale inference.
• Senior ownership and collaboration across research and engineering.

Responsibilities

• Lead large-scale data enrichment and curation initiatives across teams.
• Design and train out-of-distribution detection mechanisms for robust driverless operation.
• Build workflows for large-scale enrichment, data curation, and rare event mining.
• Improve efficiency of the ML operations stack and simulations.

Skills

Python

ML systems

Communication

Leadership

Tools

PyTorch

C++

CUDA

Distributed training

Job description

Описание

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. Wayve’s AV Core organisation builds the foundational capabilities required for assisted and fully autonomous driving. Within AV Core, the Core Model Safety team focuses on collision avoidance, model understanding, and robustness under failure. You’ll join a focused, high-impact senior team working with large-scale training and fleet data, alongside partners in research, simulation, evaluation, and applied engineering.

Задачи

You’ll lead large-scale data enrichment and curation initiatives, collaborating across pre- and mid-training, reward modelling, self-supervised labelling, and the Data Enrichment Platform. You’ll develop workflows for mining hard and rare events, investigate how models perform in the real world, and improve the efficiency and reliability of our machine learning operations stack. Lead on large-scale data enrichment within the Core Model Safety team, including collaborations with Pre/Midtraining, reward modelling, self-supervised labelling, and the Data Enrichment Platform. Build workflows for large-scale enrichment, data curation, and hard/rare event mining in support of robust model performance and introspection. Design and train out-of-distribution detection mechanisms to enable robust driverless operation. Work at the intersection of model engineering, infrastructure, compute, and data, shaping the boundaries and architecture across those teams. Use your judgement and expertise to improve the overall efficiency of the machine learning and operations stack.

Требования

Hands-on experience with ML systems deployed in the real world. Proficiency in Python and PyTorch, with strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems. A strong track record with ML operations and infrastructure, including experience with distributed computing environments and large-scale inference. Senior-level ownership and collaboration: able to lead a substantial technical area, work across research and engineering boundaries, and bring others along through clear written and verbal communication. Будет плюсом: Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems; Experience mining, generating, or evaluating rare events using simulation and fleet or heterogeneous real-world data; Experience setting up cloud-based monitoring solutions for large-scale operations, including dashboards, logging, and real-time alerts; Experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent; Proficiency in C++, CUDA, distributed training, or performance optimization for production machine learning systems

Условия

We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. Salaries benchmarked against the market annually Meaningful equity, sharing in the ownership and long term success of Wayve Relocation support and visa sponsorship where applicable Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs Learning and development budgets with support for training, conferences and growth Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more