Description
We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future.
You will be part of Wayve's engineering teams, building the AI, robotics, simulation, data, and systems foundations needed to deploy a generalisable AI Driver safely and at scale.
Your day-to-day responsibilities will include:
- Building scalable methods to discover rare, high-value, and safety-critical scenarios in fleet data.
- Using embeddings, active learning, similarity search, model-assisted mining, and smart sampling to improve dataset coverage.
- Designing pipelines for automated enrichment, labelling, deduplication, and data-quality monitoring.
- Running experiments to understand which data mixtures and curation strategies improve model performance.
- Collaborating with foundation-model, evaluation, simulation, and infrastructure teams to move ideas into production.
- Building benchmarks and slice analyses that expose model blind spots and guide the next data and modelling iteration.
You will be working on:
- Petabyte-scale real-world driving and simulation datasets.
- Data engines and retrieval systems using Python, SQL, Spark, embeddings, and multimodal models.
- Training-data strategy for embodied vision-language and driving models.
- Automated enrichment, active learning, and model-assisted labelling.
- Evaluation datasets that connect data decisions to real-world driving outcomes.
Requirements:
- Strong machine-learning and software-engineering fundamentals.
- Proficiency in Python and a modern deep-learning framework.
- Comfortable with SQL or large-scale data tools.
- Experience with data curation, foundation models, large-scale data wrangling, computer vision, or model evaluation.
- Ability to reason about sampling, class imbalance, label quality, dataset coverage, and experimental design.
Nice to have:
- Experience with autonomous driving, robotics, VLMs, active learning, similarity search, Spark, or distributed training.
Benefits:
- Salaries benchmarked against the market annually.
- Meaningful equity.
- Relocation support and visa sponsorship where applicable.
- Hybrid working and access to vehicle workshops and labs.
- Learning and development support.
- Comprehensive location-dependent health, family, retirement, and wellbeing benefits.