Machine Learning Researcher

Arkose Labs is on a mission to create an online environment where all consumers are protected from spam and abuse. As a Fast Company 2025 Best Workplace for Innovators, we provide a proactive fraud deterrence platform, Arkose Titan, designed to neutralize modern attacks powered by Agentic AI and LLMs. By combining proprietary intelligence with dynamic friction, we undermine attacker ROI to protect global giants like Microsoft, Meta, and Roblox. Headquartered in San Mateo, CA, we maintain a global presence across APAC, Central and South America, and EMEA.

Position Overview:

As a Sr. Machine Learning Researcher specializing in security, you will apply your expertise in machine learning and cybersecurity to develop innovative solutions to detect, prevent, and respond to security threats and fraud. You will conduct independent research, collaborate with cross-functional teams, and stay abreast of the latest advancements in the field.

Key Responsibilities:

• Develop and implement ML and deep learning models to detect and mitigate security threats.
• Developing new product features using statistical/machine learning-based algorithms to identify bot and fraud traffic
• Conduct data cleaning, preprocessing, and exploratory data analysis. Apply statistical methods to analyze security-related data.
• Stay updated with the latest research and advancements in ML and cybersecurity. Publish research findings in reputable journals and conferences.
• Collaborate with cross-functional teams to understand project requirements and contribute to the development of solutions.
• Collaborate with security analysts, software engineers, and other stakeholders to integrate ML solutions into security products.
• Communicate complex technical concepts to both technical and non-technical stakeholders effectively.

Must Have:

• Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Proficiency in ML algorithms, neural networks, and model development.
• Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong coding skills in Python, R, and familiarity with languages such as C++, Java, or Scala.
• Ability to perform data cleaning, preprocessing, and exploratory data analysis.
• Strong understanding of statistical methods and their applications.
• Familiarity with cybersecurity concepts, threat models, and common attack vectors.
• Experience with security tools and technologies (e.g., intrusion detection systems, firewalls, malware analysis tools).