About Mitek
Mitek (NASDAQ: MITK) is a global leader in digital identity verification, biometric authentication, and fraud prevention. Our AI-powered platform combines computer vision, biometrics, and machine learning to help organizations securely onboard users and defend against emerging threats such as deepfakes and AI-driven fraud.
Trusted by over 7,500 organizations worldwide, Mitek processes millions of identity transactions daily. Headquartered in San Diego, with a strong presence across Spain, France, Mexico, and the UK and the Netherlands. Visit us at www.miteksystems.com.we are driven by a clear mission: protect what’s real.
We are a Virtual First organization that values flexibility, collaboration, and innovation, while fostering an inclusive environment where diverse perspectives drive better outcomes.
Why Join This Team
Work on AI that fights AI-driven fraud
Direct impact on protecting users from real-world threats
Collaborate with experts across fraud, biometrics, and AI research
Opportunities for growth across teams and domains within Mitek
A culture focused on collaboration, innovation, and continuous learning
The Impact You’ll Make
As a Machine Learning Engineer, you will join our Fraud & AI Integrity group, specifically focused on deepfake detection, digital manipulation, and injection attack detection in selfie-based identity verification.
This is a hands-on, production-focused role where you will design and deploy computer vision models that operate in adversarial environments. Your work will directly contribute to protecting users and businesses from sophisticated fraud attacks powered by AI.
What You Will Do (Core Responsibilities):
- Design, train, and deploy machine learning models for image-based fraud detection, including:
Deepfake detection
Injection attack detection
Digital manipulation analysis in biometric verification
Work end-to-end across the ML lifecycle:
Dataset curation (large-scale, noisy, adversarial datasets)
Model development and training
Evaluation and iteration using fraud-relevant metrics
Production deployment and monitoring
Build robust data pipelines, including:
Data validation, cleaning, and labeling strategies
Handling class imbalance, bias, and distribution shift
Define and execute evaluation frameworks focused on real-world performance:
Precision/recall trade-offs
False positive vs. fraud detection balance
Robustness to unseen attack types
Collaborate closely with:
Fraud & AI research teams
Data collection and annotation teams
MLOps and platform engineering
Product teams
Contribute to production ML systems, ensuring:
Scalability and reliability
Monitoring and performance tracking
Continuous improvement against evolving threats
Who You Are (Soft Skills):
- A pragmatic problem-solver who understands the gap between research and production
Comfortable working in adversarial, fast-evolving problem spaces
Able to clearly communicate technical concepts and trade-offs
Collaborative and adaptable, with a strong sense of ownership
Motivated by building technology that has real-world impact
What You’ll Need (Required Knowledge, Technical Skills):
Bachelor’s degree in Computer Science, Engineering, or related field
2+ years of experience deploying machine learning models into production
Strong background in computer vision (image-based ML)
Solid programming skills in Python
Hands-on experience with PyTorch and/or TensorFlow
Experience working with real-world datasets and building data pipelines
Cloud: AWS
Languages: Python
ML Frameworks: PyTorch, TensorFlow, Scikit-learn
Data Tools: Pandas, OpenCV
Infrastructure: Docker, CI/CD, cloud-based ML pipelines
Required Qualifications:
Tech Stack
What Would be Nice (Preferred Experience):
Advanced degree (PhD or equivalent experience in Machine Learning or Computer Vision)
Experience in fraud detection or adversarial ML domains
Experience with deepfake detection, image forensics, or manipulation detection
Familiarity with generative AI models (training or analysis)
Background in data science or data engineering
What We Provide (Benefits):
Competitive package
Full Remote contract
Annual Leave
Home Office Allowance
Annual Bonus – up to 10%
Health Insurance
Learning & Development: We promote continuous learning and support role-aligned development opportunities, with access to a complimentary LinkedIn Learning licence.
Originally posted on Himalayas