Applied Scientist II - Computer Vision

Join us at Entrust

At Entrust, we’re shaping the future of identity centric security solutions. From our comprehensive portfolio of solutions to our flexible, global workplace, we empower careers, foster collaboration, and build solutions that help keep the world moving safely.

Get to Know Us

Headquartered in Minnesota, Entrust is an industry leader in identity-centric security solutions, serving over 150 countries with cutting-edge, scalable technologies. But our secret weapon? Our people. It’s the curiosity, dedication, and innovation that drive our success and help us anticipate the future.

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 a 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 Paris, France office and follows a hybrid model, requiring in-office presence three days per week.

Ready 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 today!

NO AGENCIES, NO RELOCATION