Applied Scientist II - Computer Vision

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.
• 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.
• Experiment with multimodal models to detect fraud.

You may be a good fit if

• 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.