Machine Learning Engineer, EMEA

Role overview

Lead the development and production deployment of computer vision models for biometric applications, with a focus on face analysis and recognition. The work spans model design, training data, fairness evaluation, and low-latency inference on cloud infrastructure. The role level will be determined during the interview process.

Responsibilities - Design biometric vision systems for face attributes, detection, image quality, and recognition. - Benchmark models across datasets and operating conditions, and assess fairness and disparate impact. - Train and optimize models with PyTorch, TensorFlow, and/or JAX. - Own data-to-deployment ML pipelines, including ingestion and cleaning workflows, balanced training sets, and synthetic data generation. - Prepare models for production inference using techniques such as quantization, distillation, TensorRT, or ONNX, and manage cloud deployment. - Mentor engineers, review code and designs, and support technical practices across the team.

Requirements - Multiple years of industry machine learning experience, including at least three years in biometrics or face analysis. - Deep computer vision expertise, particularly in face recognition. - Practical experience identifying, measuring, and mitigating algorithmic bias in computer vision. - Expert Python skills, including machine learning and image processing libraries, with a record of production-ready code. - Experience designing end-to-end ML pipelines and using workflow orchestration tools such as Airflow. - Experience scaling training on multi-GPU clusters and deploying services on AWS.

Nice to have - Research publications on face recognition, image quality, or fairness. - Vector database and approximate nearest neighbor search experience. - Familiarity with privacy, security, and compliance for biometric systems. - Experience deploying models to mobile or edge devices, or generating synthetic faces with GANs or diffusion models. - Strong communication skills.