3 months ago
Montréal, CanadaSenior / Staff+
Responsibilities
- Lead the design and development of biometric computer vision systems for face attributes, detection, quality assessment, and recognition.
- Architect, train, optimize, and productionize machine learning models using PyTorch, TensorFlow, and/or JAX.
- Own end-to-end ML pipelines from data ingestion and cleaning through training and deployment, including automated Airflow workflows.
- Curate balanced training datasets and generate synthetic data to address quality and diversity gaps.
- Optimize models for low-latency inference using quantization, distillation, TensorRT, and ONNX, and manage deployment on AWS.
- Conduct fairness analysis and benchmarking across datasets and operating conditions, including measuring and mitigating disparate impact.
- Mentor ML engineers, conduct code and design reviews, and establish technical best practices for the Computer Vision team.
Requirements
- At least 5 years of industry experience in machine learning, including at least 3 years dedicated to biometrics or face analysis.
- Deep expertise in computer vision, biometrics, and especially face recognition.
- Practical experience understanding, measuring, and mitigating algorithmic bias and disparate impact in computer vision.
- Expert proficiency in Python and machine learning or computer vision libraries such as Pillow, OpenCV, and PyTorch.
- Experience designing end-to-end ML pipelines and working with workflow orchestrators such as Airflow.
- Hands-on experience scaling training jobs on multi-GPU clusters and deploying services on AWS, including SageMaker, EC2, and EKS.
- Preferred experience includes research publications in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
- Preferred experience with vector databases such as Milvus or Faiss and approximate nearest neighbor search.
- Familiarity with privacy, security, and compliance in biometric systems is preferred.
- Preferred mobile or edge experience includes porting models using CoreML, LiteRT, or TFLite.
- Experience with GANs or diffusion models for generating synthetic training faces and strong communication skills are beneficial.
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About Jumio
Jumio builds identity verification, document verification, and KYC solutions for enterprises, delivered via APIs and web workflows to reduce fraud and meet compliance. Privately held and founded in 2015 with headquarters in Jaipur, India, it verifies government-issued IDs and biometrics with liveness checks from 200+ countries, serving sectors such as financial services, fintech, e-commerce, and online gaming.