[Remote] Sr. Machine Learning Engineer

Note: The job is a remote job and is open to candidates in USA. Mitek Systems is a global leader in digital and biometric identity authentication, fraud prevention, and mobile deposit solutions. As a Sr. Machine Learning Engineer, you will lead applied machine learning initiatives to enhance the identity verification engine, focusing on biometric identity verification and computer vision challenges.ResponsibilitiesBuild, train, and optimize computer vision models for image classification, face liveness detection, and presentation attack detection (PAD) / anti-spoofingWork 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 mediaDesign 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 miningDesign, 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 datasetsPrepare and curate large, noisy datasets, including data ingestion, validation, cleaning, deduplication, labeling strategies, and dataset QA to improve model reliability and generalizationDevelop evaluation protocols and success metrics that balance fraud detection effectiveness, false acceptance rates, false rejection rates, and overall business impactDevelop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, observability, and cost controlsProductionize models as resilient Python services and libraries; collaborate with platform teams to optimize APIs, latency, scalability, and operational reliabilityContribute to the evolution of our Identity Verification (IDV) platform by modernizing legacy components and improving model performance, maintainability, and modularityPartner closely with Product, Customer Success, Fraud, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, security, and reliability requirementsSupport and mentor other engineers through design reviews, code reviews, experimentation best practices, and knowledge sharingResearch 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 capabilitiesSkillsBachelor'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 systemsExperience developing and deploying computer vision models for image classification, detection, segmentation, or related image-based learning tasks in production environmentsHands-on experience designing, training, evaluating, and optimizing deep learning models using PyTorch or TensorFlowStrong computer vision background, including experience with CNNs, Vision Transformers, foundation models, image processing, and feature extraction techniquesExperience working with large-scale image datasets, including data preprocessing, augmentation, labeling strategies, dataset QA, and model evaluationUnderstanding of model performance tradeoffs, including precision, recall, false positive rates, false negative rates, and robustness in real-world environmentsProven ability to build reliable training and inference pipelines and collaborate on production deployment of machine learning systemsStrong communication and collaboration skills with the ability to work effectively across engineering, product, fraud, operations, and platform teamsExperience evaluating and improving model performance in adversarial, noisy, or highly imbalanced datasetsExperience running ML in production, including containerization (Docker), CI/CD, monitoring, model/version management, and troubleshooting data and model issues end-to-endExperience optimizing models for real-time constraints using techniques such as quantization, distillation, pruning, ONNX, and CPU/GPU inference optimizationExperience with model interpretability and debugging techniques such as Grad-CAM, saliency maps, feature visualization, error analysis, and targeted evaluationExperience 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 preferredExperience working with face-based systems, biometric image data, or adversarial computer vision problems is a strong plusExperience with synthetic data generation, domain adaptation, data augmentation, or techniques for improving model robustness and generalization in real-world environmentsBenefitsWellness: Universal, supplemental, and private healthcare plan choices based on country specificsFinancial future: retirement/pension plan contributions, MTK stock plan participationIncome protection: life event & disability coveragePaid time off: generous annual leave, company holidays, volunteer time offLearning: e-learning license, tuition reimbursement, hackathonsHome office setup allowanceAdditional/optional benefits: pet insurance, identity theft protection, legal assistanceCompany OverviewMitek Systems is a software company that specializes in digital identity verification and mobile image processing system. It was founded in 1986, and is headquartered in San Diego, California, USA, with a workforce of 501-1000 employees. Its website is https://www.miteksystems.com.Company H1B SponsorshipMitek Systems has a track record of offering H1B sponsorships, with 1 in 2026, 4 in 2025, 2 in 2024, 6 in 2023, 9 in 2022, 3 in 2021, 4 in 2020. Please note that this does not guarantee sponsorship for this specific role.