About WinnowWinnow is developing cutting edge technology to tackle the $1 trillion global food waste problem by transforming the way we produce food.Established in 2013, we are currently operating in over 70 countries, and we are just at the start of our journey.As the global leader in addressing food waste, we are committed to continuing to push the envelope on what technology can do to solve this problem. For example, Winnow Vision, our 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 been received tremendous enthusiasm from our clients and the industry. You can read more about it on<a href="https://blog.winnowsolutions.com/winnow-is-automating-food-waste-management-with-ai"> our website</a>. Other recent accolades saw Winnow listed in the 2025 <a href="https://www.thetimes.com/best-places-to-work/medium-companies/article/best-medium-companies-uk-2025-6h5mzgkwc"> Sunday Times Best Places to Work</a> - a recognition based on feedback from our UK team. While this award is based in the UK, 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. At Winnow, technology is more than code, it is a force for environmental change. If you want to apply your coding skills to solve real-world problems and contribute to a more sustainable future, we would love to hear from you.About the teamOur 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.About the roleReporting to the Head of Research Science, we are looking for an Machine Learning Scientist to join the team. Key responsibilities include:<ul><li>Designs tests and experiments to build machine-learning and AI models for detecting and recognising food and non-food items from images and videos and optionally texts, not excluding other contextual data like date, time, season, geography etc to improve the performance of the models.</li><li>Maintains a high level of data quality via directing and guiding the annotation team and third-parties in data annotation and labelling, including leveraging modern AI-assisted annotation approaches.</li><li>Manages the data efficiently for model training and evaluation.</li><li>Applies state-of-the-art model architectures and techniques (including deep learning, transformer-based and multimodal models) to improve the models.</li><li>Prepares reports and presents results to summarise main findings and conclusions</li><li>Presents results of scientific research to stakeholders and may contribute to external publications or technical articles.</li><li>Writes software and algorithms for training and running the models within Winnow applications.</li><li>Collaborates with other teams at Winnow in deploying the models to Winnow's applications to embedded systems like NVIDIA Jetson devices and to the cloud platforms such as AWS and GCP.</li><li>Explores and integrates Large Language Models (LLMs) and Vision-Language Models (VLMs) for tasks such as annotation support, detection, multimodal reasoning, and building AI-powered services and agents.</li></ul>RequirementsAbout you<ul><li>Minimum Bachelor's degree in Machine Learning, Computer Science, Mathematics, Statistics or equivalent with preferably some commercial experience.</li><li>Experience in developing machine learning and artificial intelligence models, preferably within a corporate setting.</li><li>Strong knowledge of one or more of the following areas: Object Detection, Image Classification, Action Recognition, Image Generation, or Semantic Segmentation. Experience in Bayesian methods, Reinforcement Learning, Signal Processing or NLP is a plus.</li><li>Good programming skills using Python.</li><li>Experience with modern deep learning approaches, including transformer-based models and multimodal systems (LLMs/VLMs).</li><li>Expertise in working with TensorFlow or PyTorch.</li><li>Experience adapting modern model architectures to solve real-world problems.</li><li>Familiarity with Linux and AWS.</li><li>Experience with model inference and optimisation tools (e.g. ONNX, TensorRT or similar). Experience with modern LLM serving frameworks (e.g., vLLM, llama cpp) is a plus.</li><li>Comfortable working independently, prototyping solutions, and bringing them to production.</li><li>Teamwork, knowledge transfer, process documentation.</li><li>Passionate about Machine Learning and Artificial Intelligence.</li></ul>Our technologyTechnology is at the forefront of what we do, and the success of our company is based on our world-class technology and on finding solutions to real-world problems that have not been solved to date. Our current stack includes in Machine Learning and Data Science team is: <ul><li>Python</li><li>AWS Cloud using EC2, Redshift, S3 and other services</li><li>Atlassian stack (JIRA, Confluence, BitBucket)</li><li>Reporting and Analytics using Postgres, Jupyter, Airflow and Grafana</li><li>Model inference on edge devices via ONNX and TensorRT</li><li>Model training using TensorFLow and PyTorch</li><li>Applied machine learning tooling, including embeddings, retrieval-based workflows, vision-language models and LLM-assisted data analysis, annotation and evaluation</li></ul>Benefits<ul><li>Competitive base salary </li><li>Meal tickets - 40 RON per working day</li><li>2 Wellness hours per month plus a 274 RON gross monthly wellness allowance or the option to swap the wellness allowance for a 7Card subscription </li><li>25 days of paid vacation time in addition to national holidays, plus the option to buy a further 5 days annual leave </li><li>Company part-funded private health insurance and eye care allowance</li><li>Life insurance (3 times base salary) </li><li>Company stock options package </li><li>Eligible for discretionary annual bonus</li><li>Employee Assistance Programme - 24/7 helpline for your wellbeing</li><li>Learning and development allowance of 1,730 RON annually </li><li>Hybrid way of working - we’re all in the office on Wednesdays and Thursdays</li><li>Company provided breakfast &amp; snacks on office days</li><li>Early Finish Fridays - log off at 3 PM on a Friday if you have completed your tasks by then</li><li>Our own office space with a great working environment </li><li>You will love what you do – waking up every day solving one of the biggest social problems of our generation - food waste</li><li>Committed team members with broad experience who share a common passion to build a world class business</li></ul>

Kitchens using Winnow know exactly what they're putting in their bins. At Winnow, we help the food service and hospitality industry cut down on food waste by making the kitchen smarter.With <a href="https://www.winnowsolutions.com/vision?hsLang=en" rel="noopener noreferrer" target="_blank">Winnow Vision</a>, our AI-enabled tool, kitchens can automatically track food waste. Winnow Vision uses computer vision to help chefs easily pinpoint waste, cut costs and save time. The system takes photos of wasted food as it's thrown away and, using the images, the machine trains itself to recognise what has been thrown in the bin. This means that, over time, food will be thrown in the bin and the data will be captured automatically. This increases data accuracy and ease of use.Simple and intuitive to use, the <a href="https://www.winnowsolutions.com/en/product?hsLang=en" rel="noopener noreferrer" target="_blank">Winnow Waste Monitor</a> comprises a digital scale and a connected tablet. Using any bin, team members throw food away in the usual way. The weight is recorded and then the user selects the reason and identifies the item/dish using the tablet.

Machine Learning Scientist

About WinnowWinnow is developing cutting edge technology to tackle the $1 trillion global food waste problem by transforming the way we produce food.Established in 2013, we are currently operating in over 70 countries, and we are just at the start of our journey.As the global leader in addressing food waste, we are committed to continuing to push the envelope on what technology can do to solve this problem. For example, Winnow Vision, our 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 been received tremendous enthusiasm from our clients and the industry. You can read more about it on<a href="https://blog.winnowsolutions.com/winnow-is-automating-food-waste-management-with-ai"> our website</a>. Other recent accolades saw Winnow listed in the 2025 <a href="https://www.thetimes.com/best-places-to-work/medium-companies/article/best-medium-companies-uk-2025-6h5mzgkwc"> Sunday Times Best Places to Work</a> - a recognition based on feedback from our UK team. While this award is based in the UK, 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. At Winnow, technology is more than code, it is a force for environmental change. If you want to apply your coding skills to solve real-world problems and contribute to a more sustainable future, we would love to hear from you.About the teamOur 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.About the roleReporting to the Head of Research Science, we are looking for an Machine Learning Scientist to join the team. Key responsibilities include:<ul><li>Designs tests and experiments to build machine-learning and AI models for detecting and recognising food and non-food items from images and videos and optionally texts, not excluding other contextual data like date, time, season, geography etc to improve the performance of the models.</li><li>Maintains a high level of data quality via directing and guiding the annotation team and third-parties in data annotation and labelling, including leveraging modern AI-assisted annotation approaches.</li><li>Manages the data efficiently for model training and evaluation.</li><li>Applies state-of-the-art model architectures and techniques (including deep learning, transformer-based and multimodal models) to improve the models.</li><li>Prepares reports and presents results to summarise main findings and conclusions</li><li>Presents results of scientific research to stakeholders and may contribute to external publications or technical articles.</li><li>Writes software and algorithms for training and running the models within Winnow applications.</li><li>Collaborates with other teams at Winnow in deploying the models to Winnow's applications to embedded systems like NVIDIA Jetson devices and to the cloud platforms such as AWS and GCP.</li><li>Explores and integrates Large Language Models (LLMs) and Vision-Language Models (VLMs) for tasks such as annotation support, detection, multimodal reasoning, and building AI-powered services and agents.</li></ul>RequirementsAbout you<ul><li>Minimum Bachelor's degree in Machine Learning, Computer Science, Mathematics, Statistics or equivalent with preferably some commercial experience.</li><li>Experience in developing machine learning and artificial intelligence models, preferably within a corporate setting.</li><li>Strong knowledge of one or more of the following areas: Object Detection, Image Classification, Action Recognition, Image Generation, or Semantic Segmentation. Experience in Bayesian methods, Reinforcement Learning, Signal Processing or NLP is a plus.</li><li>Good programming skills using Python.</li><li>Experience with modern deep learning approaches, including transformer-based models and multimodal systems (LLMs/VLMs).</li><li>Expertise in working with TensorFlow or PyTorch.</li><li>Experience adapting modern model architectures to solve real-world problems.</li><li>Familiarity with Linux and AWS.</li><li>Experience with model inference and optimisation tools (e.g. ONNX, TensorRT or similar). Experience with modern LLM serving frameworks (e.g., vLLM, llama cpp) is a plus.</li><li>Comfortable working independently, prototyping solutions, and bringing them to production.</li><li>Teamwork, knowledge transfer, process documentation.</li><li>Passionate about Machine Learning and Artificial Intelligence.</li></ul>Our technologyTechnology is at the forefront of what we do, and the success of our company is based on our world-class technology and on finding solutions to real-world problems that have not been solved to date. Our current stack includes in Machine Learning and Data Science team is: <ul><li>Python</li><li>AWS Cloud using EC2, Redshift, S3 and other services</li><li>Atlassian stack (JIRA, Confluence, BitBucket)</li><li>Reporting and Analytics using Postgres, Jupyter, Airflow and Grafana</li><li>Model inference on edge devices via ONNX and TensorRT</li><li>Model training using TensorFLow and PyTorch</li><li>Applied machine learning tooling, including embeddings, retrieval-based workflows, vision-language models and LLM-assisted data analysis, annotation and evaluation</li></ul>Benefits<ul><li>Competitive base salary </li><li>Meal tickets - 40 RON per working day</li><li>2 Wellness hours per month plus a 274 RON gross monthly wellness allowance or the option to swap the wellness allowance for a 7Card subscription </li><li>25 days of paid vacation time in addition to national holidays, plus the option to buy a further 5 days annual leave </li><li>Company part-funded private health insurance and eye care allowance</li><li>Life insurance (3 times base salary) </li><li>Company stock options package </li><li>Eligible for discretionary annual bonus</li><li>Employee Assistance Programme - 24/7 helpline for your wellbeing</li><li>Learning and development allowance of 1,730 RON annually </li><li>Hybrid way of working - we’re all in the office on Wednesdays and Thursdays</li><li>Company provided breakfast &amp; snacks on office days</li><li>Early Finish Fridays - log off at 3 PM on a Friday if you have completed your tasks by then</li><li>Our own office space with a great working environment </li><li>You will love what you do – waking up every day solving one of the biggest social problems of our generation - food waste</li><li>Committed team members with broad experience who share a common passion to build a world class business</li></ul>

Machine Learning Scientist

About the Job

About the Company

Winnow

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