Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here's what this particular role covers.
About our Engineering Teams

Wayve's engineering teams are building the AI, robotics, simulation, data, and systems foundations needed to deploy a generalisable AI Driver safely and at scale.

The Enrichment and Curation team transforms fleet-scale driving data into high-quality training datasets, enrichments, and evaluation benchmarks for Wayve's end-to-end AI Driver and foundation models.

Your day-to-day

• Build scalable methods to discover rare, high-value, and safety-critical scenarios in fleet data.
• Use embeddings, active learning, similarity search, model-assisted mining, and smart sampling to improve dataset coverage.
• Design pipelines for automated enrichment, labelling, deduplication, and data-quality monitoring.
• Run experiments to understand which data mixtures and curation strategies improve model performance.
• Collaborate with foundation-model, evaluation, simulation, and infrastructure teams to move ideas into production.
• Build benchmarks and slice analyses that expose model blind spots and guide the next data and modelling iteration.

What you'll 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.

You should apply if

• You have strong machine-learning and software-engineering fundamentals and can take applied research into production.
• You are proficient in Python and a modern deep-learning framework and comfortable with SQL or large-scale data tools.
• You have experience with data curation, foundation models, large-scale data wrangling, computer vision, or model evaluation.
• You can reason about sampling, class imbalance, label quality, dataset coverage, and experimental design.
• You enjoy ambiguous, cross-functional problems and communicate technical trade-offs clearly.
• Experience with autonomous driving, robotics, VLMs, active learning, similarity search, Spark, or distributed training is valuable.

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.

How we work - Locations & Flexible Working

This role is based in Sunnyvale. We operate a hybrid working model combining in-person collaboration with focused time working remotely.

The Interview Process

• Initial call / recruiter screen (25 min)
• Competency interview (60 min)
• Deep-dive interviews (Programming, System Design, ML Breadth, PyTorch Debugging; 3-4 hours)
• Final interview focused on mission, values, and level alignment

We'll always explain the format and work around your availability.

What's in it for you (location dependent)

• 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

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you'll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we'd really like to hear from you.