What You’ll Do (Essential 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.

Who You Are (Soft Skills/Attributes):

• Analytical, curious, and creative in approaching complex machine learning and computer vision challenges.
• Strong problem solver who can break down ambiguous problems, develop hypotheses, and use data to drive decisions.
• Comfortable working in adversarial domains where fraud patterns and attack methods evolve over time.
• Effective communicator who can clearly explain technical concepts, experimental results, tradeoffs, and recommendations to both technical and non-technical stakeholders.
• Collaborative team player who enjoys partnering across engineering, product, fraud, and platform teams to deliver impactful solutions.
• Self-motivated and adaptable, with the ability to manage multiple priorities in a fast-paced environment.
• Experienced in designing, implementing, testing, and maintaining production-quality software and machine learning systems.
• Strong debugging and troubleshooting skills across data pipelines, model training workflows, and production services.
• Committed to continuous learning and staying current with advancements in computer vision, deep learning, biometric authentication, fraud detection, and related technologies.

What You Need (Required Knowledge, Skills & Abilities):

• 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

What Would be Nice (Preferred Skills & Experience):

• 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.

Our Tech Stack Includes:

• Cloud: AWS (AWS-native services for AI/ML and production workloads) 
• Languages: Python 
• Data & Storage: S3, DynamoDB, MongoDB (varies by service) 
• ML Platform: SageMaker (plus standard tooling for training, evaluation, and monitoring) 
• ML Tools: Tensorflow, PyTorch, Matplotlib, Pandas, Scikit-learn, OpenCV, Pillow 
• Deployment: Containers and orchestration (ECS/EKS), CI/CD, observability