Description
We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future.
About our Data Quality & Provenance Team Our Data Quality & Provenance team sits within Wayve's AI Platform organisation and builds the evidence that helps teams make confident decisions about the data underpinning our embodied AI systems.
Your day-to-day
- Define measurable, statistically rigorous concepts of data quality, including coverage, label quality, uncertainty, provenance and model performance.
- Apply statistical-inference methods to multi-rater annotation, label ambiguity, dataset coverage and black-box model evaluation.
- Partner with annotation, autonomy and evaluation teams to translate practical quality questions into defensible metrics.
- Build automated reports that clearly communicate confidence, limitations and appropriate interpretation.
- Analyse large annotation datasets using Python and SQL to identify quality issues and inform decisions.
- Iterate on metrics and reporting based on feedback from the teams using them.
What you'll be working on
- Statistical methods for distinguishing genuine data-quality problems from label ambiguity, inconsistent annotation or evaluation noise.
- Labeler and dataset metrics that support training-data selection, simulation, annotation acceptance and model evaluation.
- Reusable quality artefacts that replace one-off analyses with registered, automated metrics and reports.
- Multi-rater and human-in-the-loop ML systems, including approaches such as Dawid–Skene-style models, psychometrics and inter-rater agreement.
- Quality standards spanning coverage, uncertainty, provenance, annotation quality and predictive-model performance.
- Integration of data-science outputs into the platform workflows used by engineering and research teams
You should apply if
- You have strong foundations in classical statistics, including experimental design, sampling, modelling, estimation and inference.
- You have practical experience with rater or annotator modelling, inter-rater agreement, label uncertainty or related methods.
- You understand data quality across dimensions such as coverage, label quality, uncertainty, provenance and model performance.
- You have worked with annotation tooling, QA methodologies, human-in-the-loop ML systems or multi-rater labelled datasets.
- You have strong Python and SQL skills and are confident analysing large, complex datasets.
- You can turn ambiguous stakeholder questions into rigorous, usable metrics and clearly communicate uncertainty, limitations and decision implications.
- Experience with probabilistic or Bayesian modelling, production platform integrations or safety-critical data would be an advantage.
What’s in it for you (Location dependant)
- 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