About the Business:
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below, https://risk.lexisnexis.com
About our Team:
IDVerse is a Sydney-based start-up that is a global pioneer in the development of digital identity
verification technology. We’ve built everything from the ground up and have a broad range of blue-chip customers across banking, telecommunications, government and more. We’ve perfected the technology locally in Australia and New Zealand and are quickly expanding into the northern hemisphere.
Join a strong team of passionate engineers and build a world-class platform to fight identity fraud at a global scale.
About the Role:
The Senior Software Engineer focuses on developing advanced systems to detect and prevent spoofing attacks during biometric authentication processes. This role involves utilizing machine learning and deep learning techniques to create models capable of distinguishing between genuine human interactions and fraudulent attempts.
Please ensure that your resume is kept between 1-2 and a max of 3 pages.

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.

Requirements:

• Machine Learning Expertise: In-depth understanding of machine learning frameworks such as TensorFlow, Keras, or PyTorch. Experience in developing deep learning models is essential.
• Programming Skills: Proficiency in programming languages such as Python, Java, or R for model development and algorithm implementation.
• Analytical Skills: Strong problem-solving abilities with a solid grasp of statistics, probability theory, and data analysis techniques.
• Collaboration Skills: Ability to work effectively with cross-functional teams (including data scientists, software engineers, and product managers) to achieve common goals.
• Experience in similar roles focusing on anti-spoofing or biometric security systems.
• Bachelor’s degree in Computer Science, Mathematics, or a related field.
• Familiarity with anti-spoofing standards for biometrics such as NIST ISO/IEC 30107 or FIDO. Innovative mindset with a passion for continuous learning and keeping up with the latest advancements in AI and machine learning technologies.

Working for you

• We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:
• Discounted Health plan rate and Optical Assistance
• Life assurance and income protection
• Option to buy additional Annual Leave days
• Employee Assistance Program
• Flexible working arrangements
• Benefits for you and your family
• Access to learning and development resources

Your recruiter will advise you on the full benefits package for your location
Learn more about the LexisNexis Risk team and how we work
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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
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• 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