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

• 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  

• 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  

Required Qualifications:

• 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 Stacks 

• Cloud: AWS  

• Languages: Python  

• ML Frameworks: PyTorch, TensorFlow, Scikit-learn  

• Data Tools: Pandas, OpenCV  

• Infrastructure: Docker, CI/CD, cloud-based ML pipelines  

• 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.