Senior Software Engineer - Computer Vision / ML Engineer

About the role

IDVerse, part of LexisNexis Risk Solutions, builds identity verification technology to fight fraud at global scale. Its systems must stay effective against evolving attacks including spoofing, deepfakes, injection attacks and AI-generated fraud. The team is globally distributed and is looking for a Senior Computer Vision / ML Engineer based in Australia with full working rights.

Key responsibilities

• Research and invent: investigate difficult problems in computer vision, biometrics, liveness, anti-spoofing, deepfake detection, generative AI and adversarial attacks, form hypotheses, run controlled experiments and explain why things work or fail.
• Red team mindset: design novel presentation, injection, deepfake, synthetic-media and AI-generated attacks, build adversarial evaluations, expose weaknesses and develop robust defenses.
• Build advanced CV/ML models: design, train, evaluate and optimize deep learning approaches on image, video and multimodal data using techniques such as representation learning, transformers, generative models and 3D/geometric vision.
• Own research through production: move ideas from prototyping through benchmarking, optimization, deployment and real-world evaluation.
• Stay close to emerging computer vision and AI research, reproduce and challenge promising ideas and turn them into product improvements.

Required profile

• Bachelor's or Master's degree in Computer Science, Machine Learning, Mathematics, Engineering or a related discipline, or equivalent demonstrated expertise.
• Highly desirable: PhD or equivalent research experience, publications, patents, open-source contributions or competition results.
• Experience with biometrics, liveness/presentation attack detection, deepfakes, adversarial ML or AI security, and red-team research.
• Familiarity with biometric security standards such as ISO/IEC 30107 is highly desirable.

Required skills

• Strong knowledge of computer vision, machine learning and deep learning, with foundations in mathematics, statistics or optimization.
• Strong Python and PyTorch or equivalent modern deep-learning frameworks.
• Experience designing controlled experiments, analyzing model behavior and failure modes, and evaluating models on challenging datasets.
• Ability to translate research into efficient, reliable production systems and strong software engineering fundamentals.
• Experience with C/C++, CUDA, TensorRT and ONNX.