Machine Learning Engineer, Data Curation & Dataset Strategy

Position: Staff Machine Learning Engineer, Data Curation & Dataset Strategy
# Staff Machine Learning Engineer, Data Curation & Dataset Strategy Wayve Technologies, Inc
· Sunnyvale, California USA
· posted on Ashby This posting mentions sponsorship in words we could not read with confidence, so we are not answering for it. The posting says: “at’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 vehic”Sign up to apply

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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 Team Wayve'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…