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eShepherd is a Vision Weigh product under Gallagher, bringing computer-vision-powered animal measurement to farms. You will take the vision module from prototype to reliable hardware running on commercial farms, with a cross-disciplinary team across data, software, firmware, hardware and product.
We value pragmatic development: move fast, test in paddocks, and iterate weekly. Office-first with Hamilton site, and a strong push to scale globally while maintaining animal welfare.
eShepherd is virtual fencing for cattle: solar-powered neckbands, a phone app and a platform that lets a farmer draw a paddock on a screen and have the mob standing in it by morning.
We run on farms in more than twenty countries, from the Burdekin in Queensland to South Dakota prairie, from Texas brush country to Hawkes Bay hill country. We have gone from startup to scale up inside Gallagher, the New Zealand company that has been making farm fencing for 88 years. We move at startup pace with the reach and patience of a business that has seen a few cycles.
Vision Weigh is the animal measurement product inside the eShepherd Farm Operating System™, sitting alongside virtual fencing and pasture intelligence. Cameras mounted in the paddock read the animal as it walks past, giving producers information they would normally only get by yarding and weighing. That data lands in the same platform that already holds the animal’s location, movement and grazing history. It is one of the hardest technical problems in our portfolio — and one of the most valuable to the farmers who need it.
You will take Vision Weigh from working prototype to hardware running reliably on commercial farms. This covers the model, the pipeline, the edge compute, the calibration, the ground truth data and the retraining loop that keeps the model accurate as deployment grows.
You will own the vision work, backed by specialists across data, software, firmware, hardware and product. The cycle here is design, test, deploy, iterate. We would rather have something useful running in a paddock this month and improve it every week than spend a year polishing something for a launch that may never happen.
You turn ideas into working products. You are the engineer who would rather build the rough version, put it in front of a real animal, find out what breaks, and go again.
Progress over perfection – test it, learn quickly, make it better.
You are comfortable with ambiguity, be a subject matter expert in computer vision, make your own calls, put mistakes on the table early, and speak up when you are stuck.
You are curious about the physical world. The interesting problems in this role happen outdoors, where animals stand at awkward angles, lenses get covered in mud, low sun ruins a frame, breeds vary enormously and two animals walk through together.
We are office first, because the best ideas come out of working closely with the people around you.
We’re looking for someone who knows how to take computer vision beyond the experiment and make it work in the real world.
Experience with depth SDKs such as Luxonis DepthAI, Stereolabs ZED or Intel RealSense, with AWS, Databricks or MLflow, with C++ on latency sensitive paths, or with livestock and agriculture, would all be highly regarded.
What we ship changes how farmers live. A bull breeder in Western Australia whose cows and calves have never done better. A grazier moving cattle on his Taranaki river flat from a deck chair in Wanaka. A Texas rancher who has freed up his labour and manages country more intensively than he ever has.
Animal welfare is the reason this product exists. Weighing and condition scoring normally means mustering, yarding and putting animals through a crush, which costs the producer time and costs the animal a stressful day. Getting the same numbers from a camera as the animal walks past to water removes all of that, and the accuracy bar we hold ourselves to is what makes it possible.
You would be joining a team that is number 8 wired, where resourcefulness beats resources and the default question is whether we can rather than why we cannot. Feedback runs in both directions, disagreements happen in the open, and the pace is real.