Machine Learning Engineer

Overview

Machine Learning Engineer to design and scale face recognition systems for biometrics in production. Own end-to-end ML development, training, and deployment on AWS.

What you'll do

• Lead design and development of computer vision systems for biometrics (face attributes, detection, quality, recognition).
• Perform fairness analysis and benchmarking of biometric models across datasets and operating conditions.
• Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX.
• Own end-to-end ML pipelines from data ingestion to deployment.
• Design automated Airflow pipelines for data ingestion and cleaning.
• Curate balanced training sets and generate synthetic data to address quality and diversity gaps.
• Optimize models for low-latency inference and manage deployments on AWS.

What you'll need

• Multiple years of industry experience in machine learning with at least 3 years dedicated to biometrics or face analysis.
• Deep expertise in computer vision and biometrics, specifically face recognition.
• Practical experience measuring and mitigating disparate impact and algorithmic bias in computer vision.
• Expert Python proficiency with vision/ML libraries such as Pillow, OpenCV, and PyTorch.
• Experience designing end-to-end ML pipelines (data to train to deploy) and using Airflow.
• Hands-on experience scaling training on multi-GPU clusters and deploying services on AWS (SageMaker, EC2, EKS).