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 Need (Required Knowledge, Technical Skills):

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 

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.