AI/ML Engineer

About Plurall AI

Plurall AI is a cybersecurity company leveraging AI technology to provide advanced anti-fraud and anti-deepfake solutions. Their proprietary AI engine swiftly analyzes digital content with exceptional accuracy to help organizations proactively identify and mitigate cyber threats. The company is dedicated to protecting businesses from sophisticated risks, enabling secure and reliable operations.

Role Overview

This full-time AI/ML Engineer position, based in New York, NY, offers a hybrid working model combining onsite and remote days. The role focuses on designing, developing, and refining machine learning models tailored for cybersecurity challenges such as fraud detection and deepfake recognition. Key tasks include researching novel pattern recognition and neural network techniques, optimizing models for performance and efficiency, and collaborating closely with security specialists and software developers to deploy solutions in production environments.

Responsibilities

• Develop and enhance machine learning algorithms for cybersecurity applications, including fraud and deepfake detection.
• Conduct research on advanced pattern recognition and neural network frameworks to improve model capabilities.
• Assess and iterate on model accuracy, inference speed, and overall effectiveness.
• Work collaboratively with security experts and engineering teams to integrate ML models into live systems.
• Monitor model performance in production and contribute to the development of scalable, secure ML pipelines.
• Document technical methodologies and participate in peer code reviews.
• Stay updated with the latest trends in AI and cybersecurity to maintain cutting-edge solutions.

Qualifications

• Strong computer science background with expertise in algorithms and scalable system design.
• Proficient in pattern recognition and neural network applications, preferably within cybersecurity or fraud detection domains.
• Solid knowledge of statistics and probability for model evaluation and data-driven decisions.
• Experience using ML frameworks and languages such as Python, PyTorch, TensorFlow, and scikit-learn, including building full ML pipelines.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or an equivalent technical discipline or relevant experience.
• Hands-on experience deploying ML models in production and optimizing inference; familiarity with cloud platforms like AWS, GCP, or Azure is expected.
• Understanding of cybersecurity principles, anomaly detection, and fraud prevention is an advantage.
• Ability to thrive in a hybrid work environment and communicate complex technical concepts effectively across teams.