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

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):

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 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 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.