Food waste is a $1 trillion problem – costing the world over 1% of global GDP. We’re dead set on solving the problem and looking for people to help us achieve our mission. We, at Winnow, believe that food is far too valuable to waste, and that technology can transform the way we produce food. Our team is made of people who all share a passion for food and technology.
Winnow was founded in London in 2013 to help the hospitality industry prevent food waste through internet of things tools in the kitchen. We have worked with thousands of sites and are operating in over 90 countries around the world supported by our offices in London, Dubai, Singapore, Cluj-Napoca (Romania) and Chicago. We are a scale-up stage company with a strong base of clients who are rolling out our system globally. We have blue-chip customers including Accor Hotels, IKEA, IHG, Marriott, Compass Group and many others.
Winnow’s clients on average reduce waste by over 50% by value and sustain savings. Winnow works with hotels, universities and schools, staff restaurants, event/hospitality kitchens, buffets, pubs, and high street restaurants. Where the system is permanently adopted, pre-consumer waste value is reduced by 50% - 70% with no detrimental impact to the perceived quality or value of the offer to their customers. This represents a typical improvement of food cost savings of 3% to 8%, commonly a 40%+ increase in profitability for operations.
As the global leader in addressing food waste, we are committed to continue pushing the envelope on what technology can do to solve this problem. Winnow Vision, our new artificial intelligence-based technology, is trained to automatically track all food waste thrown away. It has won awards at the World Economic Forum and has received tremendous enthusiasm from our clients and the industry. You can read more about it on our website and this article in Forbes.
Winnow has recently been ranked #4 in the Global FoodTech 500 for 2025, recognising our position among the world’s leading innovators at the intersection of food, technology, and sustainability. We have also consistently been recognised in the FoodTech 500, the world’s first definitive list of global entrepreneurial talent in this space, further reinforcing our impact in the industry. Other recent accolades include being listed in the 2025 Sunday Times Best Places to Work, a recognition based on feedback from our UK team. While this award is UK-based, it reflects something global: a culture built on purpose, collaboration, and the belief that businesses can - and should - tackle real-world problems while being great places to work. Winnow was also named among Impact 50’s most impactful companies to work for. You can read more about it here.
We are passionate about living our values and place them at the centre of everything we do. We are excited about like minded talent who share these values, joining us in our mission:
This is an opportunity to join an exciting organisation and help us propel our growth at what are truly the most exciting and dynamic points in time in our business. You will work alongside a driven team who are motivated by building an exciting business and leaving the world a better place than we found it.
Our Machine Learning Research team develops cutting-edge solutions to some of the world’s most complex large-scale food recognition problems. Our models have not only surpassed human-level performance, but in some areas have exceeded human expectations, enabling new possibilities in food recognition and waste reduction.
The team’s work spans the entire lifecycle of machine learning research and application, from cutting-edge research to real-world deployment. By combining rigorous experimentation, deep technical expertise, and close collaboration with other Winnow teams, our Machine Learning Research team ensures that Winnow continues to lead the way in applying AI to tackle food waste at scale.
Reporting to the Head of Research Science, we are looking for an experienced Machine Learning Engineer to join the team. Key responsibilities include:
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