Senior Research Scientist

Born in 2014, Yoti is a digital identity and biometric technology company that makes it safer for people to prove who they are. The Yoti app was designed with privacy at its core, giving people a secure way to prove their identity and share third‑party credentials with organisations and other people.

Today, we have over twenty million app downloads around the world. We’ve expanded our offering to a suite of business solutions that span identity verification, age verification and estimation, e-signing, AI anti‑spoofing technologies and we continue to think of innovative new offerings.

From day one, we’ve been working to fix an outdated identity system. This is not a journey we make on our own but with policy advisors, think tanks, researchers, academics, humanitarian bodies, our users and everyday people. We are committed to solving identity problems through grassroots research and social purpose initiatives.

Our dedication to our mission and our team is why Yoti was proudly named one of the Sunday Times Best Places to Work 2026.

To work on projects in the fields of Computer Vision, Machine Learning and Deep Learning

Reports to Head of R&D / CTO; within R&D team.

This will be a hybrid role and you will be expected to work from our Aldgate office atleast 1 day a week.

• Research on large scale classification such as face recognition, verification and face anti‑spoofing using deep learning. Investigate new deep learning network architectures, cost functions and optimisation techniques for efficient feature extraction.
• Data preparation, augmentation and preprocessing for training deep learning models. Investigate

speed‑up optimisations to train DNNs faster

• Maintain an expert level knowledge of the related academic literature on large scale classification with deep learning and being up‑to‑date with recent advances.
• To implement academic papers into functional prototypes.
• Mentoring of junior R&D team members on technical aspects, reviewing merge requests and assisting in the debugging of issues that can occur during model development.
• Presenting the evaluated performance of models to people outside the R&D team, i.e. to people in other teams.

• PhD in the areas of computer vision, machine learning, image processing, or the ability to demonstrate equivalent expertise.
• Strong knowledge of DNN theory and practical experience of applying DNN in computer vision
• Strong knowledge of at least one deep learning frameworks (Tensorflow/PyTorch/Jax)
• Proficiency with Python coding
• Proficiency working with Git inside a team, including appropriate use of branches and merge requests
• Ability to write clean and well maintained code
• Attention to detail
• Strong analytical and problem‑solving skills with the capability of implementing academic papers

into functional prototypes.

• A solid working knowledge of the Linux command line
• Working with Docker, and the ability to create new docker images for specific use cases
• The ability to work independently on the technical aspects of a self‑contained project, e.g. being able to develop a model from the specification for that model through to doing a thorough analysis of the trained model

Stage 1: Call with a talent acquisition team member (30 minutes)

Stage 2 : Call with the hiring manager (45 minutes)

Stage 3 : Coding Interview

Stage 4 : Research Presentation with the R&D Team (2 hours)

Stage 5 : Call with Head of R&D ( 30 minutes)

• Hybrid Working: Enjoy the best of both worlds with a blend of home working and time spent in our central London, dog‑friendly office.
• Core
  
  Working Hours:
  
  We focus on outputs,…