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. It is looking for a full-time, remote Senior Computer Vision / ML Engineer based in Australia. The team is remote and globally distributed, and candidates must have full working rights in Australia.

Key responsibilities

• Research and invent: investigate 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.
• Take ideas from research and 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, competition results or evidence of original technical contributions.
• Experience with biometrics, liveness/presentation attack detection, deepfakes, adversarial ML or AI security, and red-team or adversarial research experience.
• 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 concepts into efficient, reliable production systems.
• Software engineering fundamentals; experience with C/C++, CUDA, TensorRT and ONNX.