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Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. The Data Quality & Provenance team sits within Wayve's AI Platform organisation and develops statistically robust methods, metrics, and tools to determine when data-quality, annotation, and evaluation signals can be trusted.
In this role, you will define measurable, statistically rigorous concepts of data quality spanning coverage, label quality, uncertainty, provenance, and model performance. You'll apply statistical-inference methods to multi-rater annotation, label ambiguity, dataset coverage, and black-box model evaluation. You'll partner closely with annotation, autonomy, and evaluation teams to translate practical quality questions into defensible metrics and build automated reports that clearly communicate confidence, limitations, and appropriate interpretation.
Your day-to-day work involves analysing large annotation datasets using Python and SQL to identify quality issues and inform decisions, iterating on metrics and reporting based on feedback from teams using them. You'll develop statistical methods for distinguishing genuine data-quality problems from label ambiguity or evaluation noise, create labeler and dataset metrics that support training-data selection and model evaluation, and build reusable quality artefacts that replace one-off analyses with registered, automated metrics. You'll also work on multi-rater and human-in-the-loop ML systems, including Dawid–Skene-style models, psychometrics, and inter-rater agreement approaches, and integrate data-science outputs into platform workflows used by engineering and research teams.
Wayve operates a hybrid working model combining in-person collaboration in dedicated office spaces with focused remote work. Main hubs include London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company is not a mature, fully-structured place—much of how they work is still being written, and you'll help shape it.
REQUIREMENTS:
- Strong foundations in classical statistics, including experimental design, sampling, modelling, estimation, and inference
- Practical experience with rater or annotator modelling, inter-rater agreement, label uncertainty, or related methods
- Understanding of data quality across dimensions such as coverage, label quality, uncertainty, provenance, and model performance
- Experience with annotation tooling, QA methodologies, human-in-the-loop ML systems, or multi-rater labelled datasets
- Strong Python and SQL skills with confidence analysing large, complex datasets
- Ability to turn ambiguous stakeholder questions into rigorous, usable metrics and clearly communicate uncertainty, limitations, and decision implications
- Advantageous: experience with probabilistic or Bayesian modelling, production platform integrations, or safety-critical data
