SporeSense is a University of Manchester spin-out building an AI-powered device that detects airborne crop disease before any symptoms appear. Farmers can then act early, protect their yields and use fewer pesticides.
As our AI & Computer Vision Engineer, you will build the machine learning at the heart of the SporeSense product. You will work directly with the founders, scientists and customers, and spend real time in the field seeing your models at work.
The role has four core areas:
- AI and computer vision development: Design, train and evaluate the models that identify crop pathogens from images.
- Field data and trials: Collect and curate real-world image data from growers and research partners across the UK and Europe.
- System integration and deployment: Help connect cameras, sensors, embedded hardware and cloud services, and support systems running on farms.
- Product direction: Turn what growers tell us into technical priorities, and help shape where our technology goes next.
£33,000 – £38,000 per annum (Year 1); fixed term for 12 months initially, with progression subject to annual project continuation and performance review.
Location
Hybrid; Central Manchester, with hybrid flexibility for planning, reporting and remote work. The role also features some UK travel for conferences, road shows, partner meetings and grant funder events.
Working hours
Full time: Monday-Friday
Annual leave
You are entitled to 25 working days paid annual leave per leave year and paid leave for UK bank holidays on your normal working days.
What you will do
- Develop and test computer vision and machine learning models for crop disease detection
- Build and maintain image-processing and AI pipelines using modern frameworks
- Collect, label and analyse image datasets from field deployments
- Prototype new ideas quickly and take the best ones into the product
- Work with hardware and software colleagues to integrate cameras, sensors, edge devices and cloud services
- Take part in field trials, and help deploy and troubleshoot systems in real farm conditions
- Work with scientists and engineers to improve model accuracy and product performance
- Share what you learn from growers to shape technical priorities
Skills and attributes
Required from day 1
- A solid grasp of machine learning, deep learning and computer vision fundamentals
- Strong Python and C++ skills
- Experience training and evaluating models on real datasets, through university, research, an internship or personal projects
- The ability to design experiments, analyse results and debug problems
- Clear communication of technical ideas to non-technical people
- Curiosity about new AI tools and techniques
- Willingness to travel in the UK and occasionally Europe, and to work outdoors on farms when needed
- Excitement about building products, not only doing research
Desirable, not essential
- PyTorch, TensorFlow or OpenCV experience
- Edge AI, embedded systems, sensors or robotics
- MLOps, cloud deployment and Git
- Multimodal AI, foundation models or 3D imaging
- Experience taking a project from concept to working prototype
- A full UK driving licence
- An interest in agriculture, sustainability or environmental tech
Skills you'll develop in the role
- Taking AI from a dataset to a product running in the field
- Hands-on experience with edge AI, sensors and connected hardware
- A close understanding of growers, agronomists and crop science
- Broad exposure to how a deep-tech start-up is built, across technology, customers and fundraising, working directly with the founders
- Ownership of real parts of the product as the team grows
Experience needed
- A degree in AI, Computer Science, Electronic Engineering, Robotics, Machine Learning, Data Science, Physics, Maths or a related subject
- A Master's or substantial project work in AI or computer vision is a plus
- You take ownership, learn fast and are comfortable with uncertainty
- You balance scientific rigour with getting things working
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