Sr. Machine Learning Engineer

Mitek Systems is a global leader in digital and biometric identity authentication and fraud prevention solutions. As a Sr. Machine Learning Engineer, you will lead applied ML initiatives that power the next-generation Identity Verification engine, focusing on building, training, and deploying machine learning models for various identity verification use cases.

Responsibilities:

• Build, train, and ship ML models for identity verification use cases such as biometric matching, liveness / anti-spoofing, identity document processing (OCR/extraction), and fraud detection (team assignment based on experience)
• Prepare large, noisy datasets: ingestion, validation, cleaning, deduplication, labeling strategy, and dataset QA to improve model performance and reliability
• Design experiments, evaluation protocols, and success metrics (offline and online), iterate based on measurable business impact (detection rates, fraud losses, false positives)
• Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, and cost controls
• Productionize models as resilient services and libraries in Python; collaborate with platform teams on APIs, latency and observability
• Contribute to the transformation of our IDV engine: modernizing legacy components, improving modularity, and raising quality, performance, and maintainability
• Work closely with Product, Customer Success, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, and reliability requirements
• Support other engineers through design reviews, code reviews, and knowledge sharing; help raise the technical bar across the team

Requirements:

• Bachelors degree in computer science or related field (or equivalent professional experience)
• Knowledge, skills and abilities typically gained from 5+ years of experience in applied machine learning / ML engineering with strong software engineering fundamentals (or equivalent combination of education and experience)
• Strong Python skills and experience building production ready code
• Demonstrated experience solving computer vision tasks with ML models utilizing PyTorch or Tensorflow
• Strong computer vision background, including experience with CNNs, vision transformers, and foundation models
• Proven ability to work with large datasets and build reliable data preprocessing/feature engineering pipelines; comfort with distributed data tooling is a plus
• Clear communication skills and the ability to work effectively across engineering, product, and operations stakeholders
• Experience running ML in production: containerization (Docker), CI/CD, monitoring, and model/version management; ability to troubleshoot data/model issues end-to-end
• Experience optimizing models for real-time constraints (quantization, distillation, pruning, ONNX) and performance tuning for CPU/GPU inference
• Model understanding / interpretability experience (e.g., Grad-CAM, saliency maps, error slicing, and targeted evaluation)
• Experience with experiment tracking (e.g., MLflow, Weights & Biases) and strong habits around reproducibility