
Yoti helps organizations verify people's identities and ages privately and securely, both online and in person. The company offers a digital identity platform providing identity verification, age verification, document eSigning, access management, authentication, and facial age estimation using biometric and document checks. Yoti is positioned as a B2B SaaS identity provider with privacy-focused design and B Corp credentials, and its ID verification services carry ISO/IEC 27001:2013 and ISAE 3000 (SOC 2) Type 2 certifications. The platform is used globally to reduce fraud, enable age-appropriate experiences, and give people more control over their identity data.

Yoti helps organizations verify people's identities and ages privately and securely, both online and in person. The company offers a digital identity platform providing identity verification, age verification, document eSigning, access management, authentication, and facial age estimation using biometric and document checks. Yoti is positioned as a B2B SaaS identity provider with privacy-focused design and B Corp credentials, and its ID verification services carry ISO/IEC 27001:2013 and ISAE 3000 (SOC 2) Type 2 certifications. The platform is used globally to reduce fraud, enable age-appropriate experiences, and give people more control over their identity data.
Who we are
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 seventeen 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From day one, we’ve been working to fix an outdated identity system.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.
Purpose of the Role: To work on projects in the fields of Computer Vision, Machine Learning and Deep Learning
Role Dimensions: Reports to Head of R&D / CTO; within R&D team.
Principal Responsibilities:
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.
Knowledge, Skills, Qualifications and Experience:
Interview Process:
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)
What’s in it for you?
This is a great opportunity to join a company that is leading the way for innovative and responsible identity verification. We’re looking for people who can adapt to a fast-paced environment, as well as champion our brand and what we stand for. We value a positive attitude and people who have a collaborative, creative and transparent approach to solving problems.
AI Usage during the recruitment process
Please read our AI Usage in Recruitment policy to know more about how Yoti uses AI in the recruitment process and our stance on how candidates can use AI during the interview process.
We believe in equal opportunities
It takes a diverse community of passionate, talented and committed people to build a simpler, more secure way of proving identity. We’re an equal opportunity employer, so we welcome applications from people of all backgrounds, with different outlooks and experiences.
We are proud to be a Disability Confident employer and we’re committed to making our recruitment process as inclusive and accessible as possible.
If you have a disability or long-term condition and need any adjustments or support during the application or interview process, please let us know — we’ll do everything we can to support you and to enable you to bring your best self to our hiring process.
Pre-employment checks
If your application is successful please be aware that as part of our pre-employment checks:
We will check your details against fraud prevention databases. We will check identity; address match; PEPs and sanctions; bank validation, verification, fraud checks, negative data (CCJ, bankruptcy). If our investigations identify fraud or other criminal offences both when applying for a job and during your employment, we will record the details on the relevant fraud prevention databases. This information may be accessed from the UK and other countries and used by law enforcement agencies and other organisations to prevent fraud.
Please contact peopleteam@Yoti.com to get information on which fraud prevention databases we use.
Talent Pool
If we consider that you might be suitable for other roles in the future, we will keep your details so we can contact you about these other roles. If you do not want us to keep your details for this purpose, please e-mail peopleteam@yoti.com or let us know at any stage of the recruitment process. For more information please read our Applicant Privacy Notice.
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