The global leader in mobile capture and digital identity verification.
Senior Machine Learning Engineer
Location
California
Posted
10 days ago
Salary
$150K - $185K / year
Seniority
Senior
Job Description
Senior Machine Learning Engineer
Mitek Systems
• 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
Job 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
Benefits
- Wellness: Universal, supplemental, and private healthcare plan choices based on country specifics
- Financial future: retirement/pension plan contributions, MTK stock plan participation
- Income protection: life event & disability coverage
- Paid time off: generous annual leave, company holidays, volunteer time off
- Learning: e-learning license, tuition reimbursement, hackathons
- Home office setup allowance
- Additional/optional benefits: pet insurance, identity theft protection, legal assistance
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