About the role

Own accuracy for real-time, computer vision agritech global product. Harness applied science and machine learning. Turn data, hardware and customer insights into a world-first accurate product.

Key Highlights:

• Own key differentiator in accuracy outcomes
• Techy product role across data, ML and hardware
• Work closely with applied science, engineering and global customers

The Opportunity
Join growing agritech business at a point where demand is skyrocketing and accuracy really matters.

The product is live, customers are using it every day, and the business has a clear opportunity to improve how performance is measured. Lift reputation further across different products, regions and conditions.

You will define accuracy milestones, build trusted benchmarks, understand where performance varies, and drive the roadmap for improvement. Some answers may sit in ML. Others may sit in hardware, sensing, calibration, customer workflows or new approaches not yet explored.

Company Profile
This business builds technology used by global agricultural customers to make critical operational and commercial decisions.

Its platform brings together software, Computer vision and numeric data, with purpose-built hardware to support fruit sizing, grading, yield insights, workforce tracking and orchard management.

The product operates in real-world environments where conditions vary and accuracy matters.

The Role
Working closely with the Head of Applied Science and Machine Learning team, you will shape priorities, manage the ML roadmap, and turn research and performance data into practical product progress.

You will dig into performance data to understand where accuracy changes, why it changes, and what should be prioritised next.

The role spans the full system: physical inputs, image data, algorithms, hardware constraints, customer workflows and commercial impact. It is technical, but also very product-led. The focus is not accuracy for its own sake. It is accuracy that improves customer value and product trust.

Your Fit
A machine learning engineering background is not required, but you understand how models are trained, evaluated and improved. This role needs someone who can work closely with ML, data science or applied science teams and challenge thinking where needed.

Your background will include technical or data-driven product ownership,with experience solving complex product problems in environments where data is imperfect and the impact is real.
Statistical confidence matters here. Precision, recall, confidence intervals, regression analysis and hypothesis testing should be concepts you can apply in practical product decisions, not just recognise in theory.
The role will suit someone curious about hardware, sensors, calibration and physical-world constraints as part of the product solution.
Experience in agriculture, horticulture, computer vision, image recognition, sensor-based systems, model monitoring or MLOps would be useful, but is not essential.
This is a strong fit for someone who wants ownership of a product problem where measurement, ML, hardware and customer value all connect.

Please note, only candidates with the right to work in New Zealand and currently based in New Zealand will be considered.