22 Jul 2026

Senior Software Engineer II

RELX

North Sydney

22 Jul 2026

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LexisNexis Risk Solutions is seeking a Senior Software Engineer II in North Sydney to develop advanced liveness detection systems using machine learning and deep learning to prevent spoofing attacks during biometric authentication. The role requires expertise in ML frameworks, programming, and anti-spoofing standards, with a focus on building a world-class platform to fight identity fraud globally.

Core AIOn-siteFull-timeSeniorTensorFlowKeras

Senior Software Engineer II

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Core AIOn-siteFull-timeSeniorTensorFlow

Salary

Not specified

Work Location

North Sydney, New South Wales, Australia, AU

Work Model

On-site

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)

TensorFlowKerasPyTorchPythonJavaRMachine LearningDeep LearningStatisticsProbability TheoryData AnalysisAnti-Spoofing StandardsNIST ISO/IEC 30107FIDO

Soft Skills

Problem SolvingCollaborationInnovative MindsetContinuous Learning

Key Responsibilities

•Liveness Detection System Design: Develop and design liveness detection systems that utilize AI algorithms to differentiate between real and fake biometric data. This includes analyzing facial features, eye movements, and other physiological indicators.
•Deep Learning Model Development: Build and optimize deep learning models specifically for liveness detection. This involves selecting appropriate algorithms, conducting experiments, and optimizing model parameters to enhance accuracy and reliability.
•Feature Engineering: Identify and extract features from biometric data that are crucial for detecting spoofing attempts. This includes texture analysis, motion-based detection, and 3D depth analysis.
•Data Collection and Preprocessing: Collaborate with data scientists to collect, clean, and preprocess large datasets required for training liveness detection models. Ensure data integrity and suitability for model development.
•Algorithm Implementation: Implement machine learning algorithms capable of processing real time biometric data to detect inconsistencies indicative of spoofing attempts. This includes integrating multimodal approaches such as facial recognition, fingerprint scanning, and iris recognition.
•System Testing and Validation: Conduct rigorous testing of liveness detection systems to ensure performance in real-world scenarios. Validate models against various spoofing techniques to ensure robustness.
•Monitoring and Maintenance: Deploy liveness detection systems into production environments, ensuring scalability and high performance. Continuously monitor system outputs to identify any issues with accuracy or efficiency.

Senior Software EngineerMachine LearningDeep LearningBiometric SecurityAnti-SpoofingLiveness DetectionTechnologyFull-timeOn-siteNorth Sydney

Key Responsibilities

•Liveness Detection System Design: Develop and design liveness detection systems that utilize AI algorithms to differentiate between real and fake biometric data. This includes analyzing facial features, eye movements, and other physiological indicators.

•Deep Learning Model Development: Build and optimize deep learning models specifically for liveness detection. This involves selecting appropriate algorithms, conducting experiments, and optimizing model parameters to enhance accuracy and reliability.

•Feature Engineering: Identify and extract features from biometric data that are crucial for detecting spoofing attempts. This includes texture analysis, motion-based detection, and 3D depth analysis.

•Data Collection and Preprocessing: Collaborate with data scientists to collect, clean, and preprocess large datasets required for training liveness detection models. Ensure data integrity and suitability for model development.

•Algorithm Implementation: Implement machine learning algorithms capable of processing real time biometric data to detect inconsistencies indicative of spoofing attempts. This includes integrating multimodal approaches such as facial recognition, fingerprint scanning, and iris recognition.

•System Testing and Validation: Conduct rigorous testing of liveness detection systems to ensure performance in real-world scenarios. Validate models against various spoofing techniques to ensure robustness.

•Monitoring and Maintenance: Deploy liveness detection systems into production environments, ensuring scalability and high performance. Continuously monitor system outputs to identify any issues with accuracy or efficiency.