Job Title: Machine Learning Engineer (Computer Vision and Biometrics)
Company: Jumio, a B2B technology company that uses AI, biometrics, machine learning and liveness detection to fight online identity fraud and financial crime. It is a leading provider of identity verification, eKYC and AML solutions.
Job Type: Full-time
Location: Remote, anywhere in the world
Salary/Pay: Not specified
Experience: Multiple years of industry experience in machine learning, with at least 3 years in biometrics or face analysis. The final job level will be set after the interview process.
Skills Required:
• Deep expertise in computer vision and biometrics, especially face recognition
• Expert Python, including vision libraries such as Pillow, OpenCV and PyTorch, with clean, production-ready code
• Experience building models with PyTorch, TensorFlow and/or JAX
• Understanding of algorithmic bias in computer vision, with practical experience measuring and mitigating disparate impact
• Experience designing end-to-end ML pipelines (data to training to deployment) with orchestrators such as Airflow
• Hands-on experience scaling training on multi-GPU clusters and deploying on AWS (SageMaker, EC2, EKS)
• Model optimization for low-latency inference (quantization, distillation, TensorRT/ONNX)
• Strong communication skills
Nice to have: Publications at CVPR, ICCV, ECCV or FG on face recognition, image quality or fairness; experience with vector databases (Milvus, Faiss) and approximate nearest neighbor search; familiarity with privacy, security and compliance in biometric systems; experience porting models to mobile or edge devices (CoreML, LiteRT, TFLite); experience generating synthetic faces with GANs or diffusion models.
Application Deadline: November 7, 2026
Description:
Jumio is hiring a Machine Learning Engineer to lead the design and scaling of face recognition systems in production, owning ML systems end to end on AWS.
You will:
• Lead the development of computer vision systems for face detection, attributes, quality and recognition
• Run rigorous fairness analysis and benchmarking of biometric models across datasets and operating conditions
• Build, train and optimize models in PyTorch, TensorFlow and/or JAX
• Own ML pipelines from data ingestion to deployment, including curating balanced training sets and generating synthetic data
• Optimize and deploy models for low-latency inference on AWS
• Mentor ML engineers and drive best practices through code and design reviews
How to Apply: Apply through We Work Remotely.