Sr. Machine Learning Engineer- Computer Vision (Liveness Detection & Spoofing)

Mitek Systems is a global leader in digital and biometric identity authentication. As a Sr. Machine Learning Engineer, you will lead applied ML initiatives focusing on computer vision and image-based machine learning problems, developing systems for identity verification and fraud detection.

Responsibilities:

• Build, train, and optimize computer vision models for image classification, face liveness detection, and presentation attack detection (PAD) / anti-spoofing
• Work on real-world identity verification and biometric authentication problems, improving model performance on noisy, adversarial inputs such as spoofed images, replay attacks, deepfakes, and synthetic media
• Design and run experiments to improve model accuracy, recall, robustness, and fraud detection performance using techniques such as augmentation, class balancing, architecture tuning, and hard-negative mining
• Design, train, and improve deep learning models (e.g., CNNs, Vision Transformers, and foundation models), including loss function design, hyperparameter optimization, and performance tuning on large-scale image datasets
• Prepare and curate large, noisy datasets, including data ingestion, validation, cleaning, deduplication, labeling strategies, and dataset QA to improve model reliability and generalization
• Develop evaluation protocols and success metrics that balance fraud detection effectiveness, false acceptance rates, false rejection rates, and overall business impact
• Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, observability, and cost controls
• Productionize models as resilient Python services and libraries; collaborate with platform teams to optimize APIs, latency, scalability, and operational reliability
• Contribute to the evolution of our Identity Verification (IDV) platform by modernizing legacy components and improving model performance, maintainability, and modularity
• Partner closely with Product, Customer Success, Fraud, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, security, and reliability requirements
• Support and mentor other engineers through design reviews, code reviews, experimentation best practices, and knowledge sharing
• Research and evaluate emerging techniques in face liveness detection, presentation attack detection (PAD), deepfake detection, biometric authentication, and adversarial machine learning to strengthen our fraud prevention capabilities

Requirements:

• Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field (or equivalent professional experience)
• 5+ years of experience in applied machine learning, computer vision, or ML engineering with strong software engineering fundamentals (or equivalent combination of education and experience)
• Strong Python programming skills and experience building production-quality machine learning systems
• Experience developing and deploying computer vision models for image classification, detection, segmentation, or related image-based learning tasks in production environments
• Hands-on experience designing, training, evaluating, and optimizing deep learning models using PyTorch or TensorFlow
• Strong computer vision background, including experience with CNNs, Vision Transformers, foundation models, image processing, and feature extraction techniques
• Experience working with large-scale image datasets, including data preprocessing, augmentation, labeling strategies, dataset QA, and model evaluation
• Understanding of model performance tradeoffs, including precision, recall, false positive rates, false negative rates, and robustness in real-world environments
• Proven ability to build reliable training and inference pipelines and collaborate on production deployment of machine learning systems
• Strong communication and collaboration skills with the ability to work effectively across engineering, product, fraud, operations, and platform teams
• Experience evaluating and improving model performance in adversarial, noisy, or highly imbalanced datasets
• Experience running ML in production, including containerization (Docker), CI/CD, monitoring, model/version management, and troubleshooting data and model issues end-to-end
• Experience optimizing models for real-time constraints using techniques such as quantization, distillation, pruning, ONNX, and CPU/GPU inference optimization
• Experience with model interpretability and debugging techniques such as Grad-CAM, saliency maps, feature visualization, error analysis, and targeted evaluation
• Experience with biometric authentication, face recognition, face liveness detection, presentation attack detection (PAD), anti-spoofing, deepfake detection, identity verification, or related fraud detection systems is strongly preferred
• Experience working with face-based systems, biometric image data, or adversarial computer vision problems is a strong plus
• Experience with synthetic data generation, domain adaptation, data augmentation, or techniques for improving model robustness and generalization in real-world environments