wayve - MLE, Data Enrichment

• 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 machine learning 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.