MLE, Data Enrichment

🧠 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 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.