Senior Machine Learning Research Engineer, Winnow

Salary not provided

+ Company stock options package

AngularJS

AWS

GCP

Python

Java

Linux

Node.js

Android

Postgres

Airflow

Tensorflow

C++

Rust

Redshift

Grafana

PyTorch

Sagemaker

JIRA

Confluence

Atlassian

Senior and Expert level

London

More information about location

Digital tools driving improvements in kitchen production

Be an early applicant

Digital tools driving improvements in kitchen production

101-200 employees

B2BArtificial IntelligenceSustainabilityFoodSaaSAPIData Analysis

Be an early applicant

Salary not provided

+ Company stock options package

AngularJS

AWS

GCP

Python

Java

Linux

Node.js

Android

Postgres

Airflow

Tensorflow

C++

Rust

Redshift

Grafana

PyTorch

Sagemaker

JIRA

Confluence

Atlassian

Senior and Expert level

London

More information about location

101-200 employees

B2BArtificial IntelligenceSustainabilityFoodSaaSAPIData Analysis

Company mission

To connect the commercial kitchen, create a movement of chefs, and inspire others to see that food is too valuable to waste.

Job

Company

Role

Who you are

Minimum Master's degree in Machine Learning, Computer Science, Mathematics, Statistics or equivalent. Ideally PhD level of above with commercial experience
Experience in developing and deploying ML models end-to-end gained in at least one corporate environment
Expertise in at least one of: Bayesian Learning, Reinforcement Learning, Object Detection, Image Classification, Action Recognition, Image Generation, Semantics Segmentation. Signal Processing and NLP are an additional bonuses
Expertise in working with TensorFlow or PyTorch
Expertise in adopting state-of-the-art model architectures and customising them to support new business requirements
Experience working with Linux and AWS
Experience running model inference technologies like ONNX, TensorRT, Ollama and vLLM
Good programming skills using Python
Working independently, prototyping and finding solutions that can be productionised
Teamwork, knowledge transfer, process documentation
Passionate about Machine Learning and Artificial Intelligence

What the job involves

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 ML Research Scientist to join the team
Designs tests and experiments to build machine-learning 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
Maintains a high level of data quality via directing and guiding the annotation team and third-parties in data annotation and labelling
Manages the data efficiently for model training and evaluation
Applies state-of-the-art model architectures and techniques to improve the models
Prepares reports and presents results to summarise main findings and conclusions
Presents results of scientific research to stakeholders and conferences, and publishes articles outlining the methodology and results of research undertaken
Writes software and algorithms in training and running the models within Winnow applications
Collaborate with other teams at Winnow in deploying the models to Winnow's applications to embedded systems like NVIDIA Jetson devices and to the cloud like AWS and GCP

Salary benchmarks

Our take

Food waste costs the hospitality industry over 100bn annually. Kitchens can waste up to 20% of food purchased, often equivalent to their total net profits. This is because chefs too often lack the necessary tools to accurately measure and manage waste. What gets measured gets managed, and by using data intelligently, kitchens can be made more efficient. This is what Winnow has set out to achieve.

The company has developed technology to help chefs achieve greater visibility in their kitchens and make better decisions that lead to dramatically reduced food waste and costs. It does this via the Winnow Waste Monitor, a set of smart scales, and an accompanying tablet app that lets kitchen staff easily log what food is thrown away. That data is then uploaded to the cloud, analysed by Winnow’s algorithm, and disseminated so that food waste can be reduced

The company has IKEA, Compass Group, and AccorHotels as customers, and estimates its clients are saving 4,300 tonnes of food waste annually by using Winnow. As well as helping kitchens to be more environmentally friendly, it also saves them money (restaurants are an industry where margins tend to be thin). With the growing trend towards sustainability and cutting down food waste, Winnow should have a growing market to expand into.

Kirsty

Company Specialist at Welcome to the Jungle

Insights

B Corporation

Top investors

43% female employees

7% employee growth in 12 months

Glassdoor (4.2)

Company

Funding (last 2 of 6 rounds)

Jun 2023

$10m

SERIES C

Oct 2019

$12m

SERIES B

Total funding: $34m

Company HQ

Old Street, London, UK

Leadership

Mark Zornes

(Co-Founder)

Graduated from INSEAD with an MBA in International Buisiness. Previously an Engagement Manager at McKinsey & Company.

Kevin Duffy

(Co-Founder)

Graduated from INSEAD with an MBA in Business Administration and Management. Previously an Investment Banker at BNP Paribas, and a Consultant at Capco.

Share this job

View 5 more jobs at Winnow