Mitek Systems

Machine Learning Engineer (Deepfake & Injection Attack Detection / Face Liveness

Rol remoto de Machine Learning Engineer con fit claro de ubicación del candidato.

Publicado19 jul 2026

Países elegibles1 país aceptado

Señal de seniorityMiddle

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

España

AWS CI/CD Docker Python

Resumen del rol

Machine Learning Engineer (Deepfake & Injection Attack Detection / Face Liveness

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

Details

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
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
Contribute to production ML systems, ensuring:
Scalability and reliability
Monitoring and performance tracking
Continuous improvement against evolving threats
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
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
Cloud: AWS
Languages: Python
ML Frameworks: PyTorch, TensorFlow, Scikit-learn
Data Tools: Pandas, OpenCV
Infrastructure: Docker, CI/CD, cloud-based ML pipelines
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
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.

What You Will Do (Core Responsibilities):

Design, train, and deploy machine learning models for image-based fraud detection, including:
Deepfake detection
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)
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
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
Precision/recall trade-offs
False positive vs. fraud detection balance
Robustness to unseen attack types
Contribute to production ML systems, ensuring:
Scalability and reliability
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 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

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.

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