AI Training Data Engineer

Every model we ship is only as good as its data. Blicker reads utility meters in production every day. Lapwing, our new LiDAR/3D AI product for the construction sector, is learning to understand underground assets. And our client systems in industry and logistics all depend on datasets that were selected, labelled, and monitored with care. Right now that care is spread across our AI engineers, in between their model work.

We're looking for the person who makes it their job.

You own the data side of every model we train. Today, our six AI engineers and consultants each carry a piece of it — selecting data, writing annotation instructions, checking label quality, watching for drift. It works, but it doesn't scale, and it isn't the sharpest use of their time. You take it over, run it better than we do now, and build it into something that scales with us.

A normal week looks like: curating the next training set (which of Blicker's new meter types are underrepresented? which Lapwing scans cover the edge cases?), reviewing annotations from our student pool and sharpening the instructions where labels disagree, checking whether production data has drifted since last month and assembling a retraining set if it has, discussing new features for our labelling and data management software with the software engineering team and running the annotation operation itself — scheduling students, onboarding new ones, and resolving the edge cases they surface.

You sit in the middle of the AI team, and your work directly determines how good their models can be.

This is a new role. There is no predecessor's playbook — you define how data operations works at Clockworks, with the AI team beside you but the ownership genuinely yours.

• Own the dataset lifecycle for Blicker, Lapwing, and client projects: selection, versioning, quality metrics, dataset management

• Monitor drift in production data and assemble retraining datasets when reality shifts, and build/automate the systems that monitor this

• Write and sharpen annotation instructions, guard label quality, and troubleshoot the edge cases annotation surfaces

• Run and grow our student annotation pool: recruiting from (tech) universities (Delft, Eindhoven, Nijmegen, etc.), onboarding, scheduling, quality feedback

• Build the tooling and process that make all of this repeatable — you decide what good data ops looks like here

You sit in the middle of our AI and software engineering teams. You won't just be managing data; you’ll actively collaborate with the AI engineers to design model experiments based on your data insights, and work closely with software engineers to refine our internal data tools and infrastructure. Your perspective on the data will directly influence the development of our models and the systems that support them.

Our annotation pool is more than labelling capacity — it's also where we find our future computer vision/AI engineers. We want it to become something with a reputation: a bigger pool, a proper communication channel, workplaces in our office for students who want to come in, students joining our Thursday drinks and demos, and a party once/twice a year — for them and for the team.

None of this is a requirement of the role; the data work stands on its own. But if building that community sounds like your kind of challenge, it's yours to claim, and we'd love you to claim it.

Must-haves:

• A beta BSc/MSc — computer science, mathematics, AI, physics, or similar

• Structured and precise
  : you enjoy getting details right and keeping an operation running, week after week

• Comfortable with Python and data tooling — or a demonstrated track record of picking up tools fast

• A feel for what makes training data good — class balance, edge cases, label consistency, drift — or the drive to master it quickly

• Comfortable instructing people and giving direct, kind feedback: you'll be running a student team

Nice-to-haves:

• Hands-on annotation experience or familiarity with annotation tooling — time in our own pool counts double

• Familiarity with computer vision models and their failure modes

• Organiz…