Applied Scientist II

About the Team:You'll be joining the team leading Entrust's Identity portfolio, formerly known as Onfido (an AI- powered digital identity solution). Our technology helps businesses verify real identities using machine learning, ensuring secure remote customer onboarding. By assessing government- issued identity documents and facial biometrics using state-of-the-art machine learning, we provide companies with the assurance they need to operate securely while allowing people to access services quickly and safely.Our Applied Scientist team consists of about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base.Position Overview:We are looking for an Applied Scientist II to design and train cutting-edge machine learning solutions related to digital identities. Join our team and work on challenging problems in deepfake detection, bias mitigation, document understanding, anomaly detection and/or efficient ML.What you will be doing:Push the frontier of research in areas such as deepfake detection, bias mitigation, fraud/anomaly detection, face matching, document understanding, and efficient on-device ML.Publish research results in national and international conferences and scientific journals. Work with product and engineering to improve our world-class identity-focused products.Representative work:Implement bias-mitigation strategies to build fair face-matching and deepfake-detection models.Train and benchmark large-scale vision-language models for document extraction.Train a multi-modal document understanding model from scratch using synthetic data.Optimise LoRA adapter latency in PEFT/Triton.Profile, debug and improve model training speed on multiple GPUs.Create a large-scale dataset for deepfake detection.Experiment with multimodal models to detect fraud.You may be a good fit if you:Have strong experience in machine learning and computer vision.Have a strong record of successfully delivering high-performance ML-driven products.Have a deep understanding of machine learning theory.Have strong coding skills in Python and PyTorch.Care about building fair and cutting-edge machine learning products.Strong candidates may also have:Technical experience in one or more of the following areas: face matching, bias mitigation, anomaly detection, document understanding or on-device ML.Published at top-level machine learning conferences. Experience optimising (distributed) training code.Where you will be: This role is based in our London, UK office and follows a hybrid model, requiring in-office presence three days per week.Benefits UK 25 days annual leave plus a day off for your Birthday.Two paid volunteering days per year.*Bupa Private Medical and Dental Insurance*Pension with The People’s Pension (employer contribution 4% of base salary)*Generous paid parental leaveLife enrichment allowance of up to £80 per month to use for services including gym, yoga, fitness classes, massages, childcare, and therapyDedicated learning opportunities including using tools like Linkedin Learning with availability to use for learning resources such as books, coaches, conferences, courses, podcasts, and moreOur open and transparent culture is reflected in our “Better Together” motto and we bring this to life by meeting once a week for our global weekly roundup (OnThursday); holding quarterly team socials, and other company-wide social eventsExpense up to £300 (or local equivalent) to purchase workstation setup equipmentThe opportunity to become a member of Entrust’s resource groups in order to learn different skills in our belonging groupsReady to Make an Impact?If youʼre excited by the prospect of working on cutting-edge machine learning for problems that matter, Entrust is the place for you. Join us in making a difference. Letʼs build a more secure world—together.Apply todayNO AGENCIES, NO RELOCATION#LI-GR1#ENT123