MLE, Data Enrichment

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 Data Enrichment Teams Our Data Enrichment team builds the ground-truth datasets and classifiers that describe complex driving scenes across Wayve's data corpus. Drawing on Wayve's World Foundation Model, we help teams find rare scenarios, expose performance gaps and develop better perception and driving models, supporting the safety and reliability of Wayve's ADAS and AI Driver products. As a Machine Learning Engineer, Data Enrichment, you will tackle computer vision problems spanning road geometry, spatial relationships, road users and driving behaviour. 🧠 Your day-to-day • Work with internal customers to define scene and scenario attributes, dataset requirements and measurable acceptance criteria for annotations and classifiers • Design and refine annotation tasks and labelling instructions, using pilots, annotator agreement and expert review to resolve ambiguity and measure quality • Curate and deliver ground-truth datasets for other teams to train and evaluate models, addressing rare scenarios, class imbalance and coverage across countries and vehicle platforms • Build and evaluate computer vision models that draw on Wayve's World Foundation Model to identify scene attributes and driving behaviours • Use error analysis to identify gaps in data, labels and model performance, and test approaches to improve them 🧩 What you’ll be working on • Ground-truth datasets and classifiers that describe road geometry, spatial relationships, road users and driving behaviour • Integrating classifiers with enrichment infrastructure to run inference across existing and newly collected driving data, making predictions available for corpus analysis and scenario mining • Improving reusable tools for annotation, training, evaluation and inference • Documenting dataset and classifier quality, and communicating limitations to colleagues across disciplines 🙌 You should apply if • You have practical experience developing and evaluating computer vision models for image or video understanding, including interpreting spatial relationships in visual data • You have strong Python programming skills and experience with a modern machine learning framework such as PyTorch • You have experience curating training and evaluation datasets and assessing annotation quality, with an understanding of class imbalance, data leakage and representative evaluation • You have taken models beyond experimentation into use, with reproducible training, evaluation and inference • You can investigate model failures and determine whether improvements need better data, clearer labels or changes to the modelling approach • You can translate an ambiguous customer request into clear technical deliverables and quality criteria, and communicate performance and limitations to colleagues from different disciplines • Desirable: experience with 3D geometry, multi-camera systems or temporal scene understanding; autonomous driving, ADAS or robotics; adapting pretrained, foundation or vision-language models; human annotation workflows, active learning or model-assisted labelling; SQL, distributed data processing and batch inference 🌱 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.