Staff Machine Learning Engineer, Data Curation & Dataset Strategy

📋 Job Overview

Application

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 Wayve

Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines.

Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility.

🎯 The Role

🧠 Your day-to-day
Build and own the ingestion-time model that scores incoming driving data and routes it to hot, cold, archive or delete
Work with training teams to define the right data distributions for every model we're building
Deliver early storage-cost savings through practical heuristics, then develop ML-driven filtering
Partner with ingestion, annotation, retrieval, data quality, model training, AV/robotics, evaluation and storage teams to set company-wide data policy
Lead and work hands-on with a small group of software and ML engineers to build data provisioning and data provenance features into Corpus

✅ Key Responsibilities

• Defining our dataset content strategy: which data and metadata we need, and why, so training teams can select the most valuable data as smoothly as possible
• Steering data selection by what our model engineers need right now. For example, if we're weak at unprotected right turns at traffic lights, those scenarios go straight to hot storage
• Delivering large, measurable storage-cost savings as ingestion scales across our internal fleet and partner and OEM data
• Evolving from practical heuristics to ML-driven filtering, and influencing what gets filtered at the vehicle and edge
• Working as a pair with our Corpus Implementation Engineer to turn strategy into production systems

📌 Required Qualifications

• You have strong autonomous-vehicle or robotics experience, working with large-scale driving, sensor or comparable data
• You're a principal, hands-on ML engineer who has built models that score, select or filter data
• You've operated at petabyte scale and are comfortable with the engineering that comes with it
• You're strong in Python and Spark, with large-scale cloud data experience
• You have strong quantitative intuition and use it to make practical, measurable decisions
• You're pragmatic and high-ownership, and can set direction across several teams without direct authority

⭐ Desirable Experience

• You bring experience in data selection, retention or filtering for model training, edge or vehicle-side filtering, computer vision or high-volume IoT data, or leading small cross-functional groups (any of these is a bonus)

🎁 Benefits

💰 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 benefit

🛂 Visa & Eligibility

Relocation support and visa sponsorship where applicable