Our engineering organization spans autonomy, computer vision and machine learning, embedded and hardware systems, full-stack, and cloud, all working in parallel across multiple active product lines tied to live customer deployments. It’s a technically deep, fast-moving team where individual contributors carry real accountability and the work shows up directly in customer operations. Ground truth quality sits at the centre of that — the annotated data this role owns is what our models are trained and measured against.
We are looking for an ML Annotation QA Engineer to own the quality of annotated data across our computer vision and machine learning programs. This role is responsible for the judgment-heavy analysis that cannot be reliably outsourced, for the decision rules behind it, and for turning annotation output into an ongoing read on how our systems are actually performing in the field.
We work with an external annotation partner at production volume, and that continues. What we need in house is someone who can analyse the annotated data, make and defend the calls the vendor cannot make consistently, and build the aggregate view that shows which facilities and equipment are degrading and why. Annotation drift, a model regression, a tool bug, and genuine field degradation all look similar in a chart and require completely different responses — telling them apart is the core of the job.
You will work closely with Machine Learning Engineers, QA, and Engineering, and the first assignment is our warehouse forklift vision program, where barcode readability and localisation analysis are the immediate need. From there the remit grows with us: drone imagery annotation today, and new task types as customer-driven capabilities come online. Success in this role requires a combination of analytical rigor, sound judgment under ambiguity, and clear written communication.