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 datasets TeamWayve's datasets team own the driving data that our AI Driver learns from, covering ingestion, annotation, retrieval and data quality. As our data grows from hundreds of petabytes towards exabyte scale, deciding what we keep, and where, is one of the biggest levers on both model quality and cost. This role sits at the centre of that challenge, shaping the data that model engineers train on across the company.
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
What you'll be working on- 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
You should apply if- 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
- 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)
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:Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives.
The Interview Process:Our process is clear and respectful of your time:
- Initial call / recruiter screen (30 mins)
- Intro with the hiring manager (1 hour)
- Competency interviews: system design and technical leadership (2 hours total)
- Deep dive: domain interview (1 hour)
- Final interview: mission & values alignment (1 hour)
We'll always explain the format and work around your availability.
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
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.
