Machine Learning Engineer

About the Role

We're on the lookout for a Machine Learning Engineer to spearhead the evolution of our device intelligence and fingerprinting systems. This isn't just a role; it's a chance to lead groundbreaking projects that directly combat fraud and enhance security for millions.

Responsibilities

• Design and refine backend services using Go (Golang) to process and analyze device data, ensuring robustness and scalability.
• Collaborate closely with software engineers, product managers, and other stakeholders to integrate machine learning capabilities seamlessly into our products.
• Develop sophisticated algorithms leveraging high-entropy signals and probabilistic matching to revolutionize device identification.
• Dive into vast datasets to uncover insights, boosting the accuracy and reliability of our systems.
• Apply advanced machine learning models to enhance device recognition and effectively manage uncertainties.
• Maintain the highest standards of privacy and security, aligned with industry best practices and regulations.
• Foster a culture of continuous learning, and document processes clearly to ensure consistency across the team.

Requirements

• 5+ years of experience in software engineering, with a focus on backend development; proficiency in Go or a similar language is essential.
• Bachelor's or Master's in Computer Science, Engineering, or a related discipline.
• Hands-on experience with applied machine learning and data-informed optimization, working with large-scale datasets using tools like PyTorch and Scikit-learn.
• Proficient in SQL for querying and analyzing large datasets.
• Comfortable working with both relational and non-relational databases.
• Exceptional problem-solving abilities, stellar communication, a collaborative spirit, and an insatiable curiosity for continuous learning.

Nice-to-Haves

• A strong understanding of cybersecurity principles, especially in device identification and fraud prevention.
• Experience managing cloud infrastructure (AWS, Google Cloud, or Azure).
• Knowledge of containerization tools (Docker, Kubernetes) and CI/CD pipelines.
• Understanding of modern browser APIs and high-entropy data collection techniques.