ML Annotation QA Engineer

Job Title: ML Annotation QA Engineer

Are you ready to build the future of supply chain? At Gather AI, we’re not just creating software, we’re pioneering a new era of warehouse intelligence. We’ve developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining "on-time, in full" delivery.

If you’re looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We’re leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.

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.

• Own the judgment-heavy quality analysis on annotated data that cannot be reliably outsourced — working the daily review queue and producing verdicts and root cause in house.

• Own, version, and refine the verdict taxonomy, decision rules, and quality guidelines for the categories you cover.

• Build and maintain performance trackers over annotated data — error rates by facility, site, equipment, and data format over time, against an agreed baseline.

• Detect anomalies against that baseline and flag them the day they appear rather than weeks later.

• Run root cause analysis on flagged anomalies, distinguishing annotation error from model or system error from genuine degradation in the field.

• Report findings to engineering and ML with reproducible evidence and stated confidence, fast enough that the issue is still observable.

• Identify systematic failure patterns rather than one-off misses, and maintain a