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

As a Machine Learning Engineer in the Fraud & AI Integrity group, you will focus on deepfake detection, digital manipulation, and injection attack detection for selfie?based identity verification.What You Will Do (Core Responsibilities)Design, train, and deploy machine learning models for image?based fraud detection, including deepfake detection, injection attack detection, and 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, 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, and product teams to contribute to production ML systems ensuring scalability, reliability, monitoring, performance tracking, and 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.Tech StackCloud: AWSLanguages: PythonML Frameworks: PyTorch, TensorFlow, Scikit?learnData Tools: Pandas, OpenCVInfrastructure: Docker, CI/CD, cloud?based ML pipelinesWhat Would be Nice (Preferred Experience)Advanced degree (PhD or equivalent 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: continuous learning with complimentary LinkedIn Learning licence.Salary€50,000 - €75,000 a year.Working ModelRemote within Spain.Occasional in?person collaboration (weekly or biweekly is a plus).#J-*****-Ljbffr

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. Tech Stack 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 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: continuous learning with complimentary LinkedIn Learning licence. Salary €50,000 - €75,000 a year. Working Model Remote within Spain. Occasional in?person collaboration (weekly or biweekly is a plus). #J-*****-Ljbffr