Machine Learning Engineer

Mitek Systems is a global leader in digital and biometric identity authentication solutions. As a Machine Learning Engineer, you will participate in applied ML initiatives to enhance their Identity Verification engine, working on model design, training, and production monitoring to deliver efficient and reliable models.

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 paired with knowledge, skills and abilities typically gained from 2-5 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