Machine Learning Engineer, Threat Detection

at  Keeper Security Inc

We are seeking a highly motivated and experienced Machine Learning Engineer to join our AI & Threat Analytics team. This is a 100% remote position with an opportunity to work a hybrid schedule for candidates based in the El Dorado Hills, CA or Chicago, IL metro area!
Keeper’s cybersecurity software is trusted by millions of people and thousands of organizations, globally. Keeper is published in 21 languages and is sold in over 120 countries. Join one of the fastest-growing cybersecurity companies and play a critical role in building Keeper’s next-generation autofill and classification models in our browser extension.

REQUIREMENTS

• 3+ years of professional experience in machine learning research or development, with a focus on security applications (preferably PAM or similar domains)
• Strong programming skills in Python or a similar language for ML development
• Hands-on experience with anomaly detection, risk modeling, and behavior analysis in security contexts
• Proficiency with ML frameworks like Scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers
• Hands-on experience with building pipelines and working with data formats such as JSON, S3, and Redshift is a plus
• Solid understanding of secure coding practices and the principles of zero-trust security
• Experience with cloud platforms (AWS, GCP, Azure) and deployment of machine learning models
• Experience using LLMs and video/image processing tools (e.g., OpenCV, YOLO)
• Strong analytical skills, problem-solving abilities, and the ability to translate complex issues into clear solutions
• Excellent communication skills and the ability to work collaboratively across teams
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Cybersecurity, or a related discipline (or equivalent experience)
• Due to this role’s involvement in GovCloud, all applicants must be a US Person

ABOUT THE ROLE

You will play a critical role in advancing Keeper’s platform by developing systems that detect anomalous behavior, enhance access control policies, and identify threats to privileged accounts. You will design, implement, and optimize advanced machine learning models that leverage data from Keeper’s Advanced Reporting and Monitoring (ARAM) platform, including system logs, mouse and keystroke data, and session recordings from Keeper’s Privilege Access Management (PAM) platform.
This role requires expertise in data engineering, analytics, and ML operations, with experience working on large language models (LLMs) and vision-based ML systems to analyze user behavior, detect threats, and generate actionable insights. You’ll collaborate closely with cross-functional teams to ensure that Keeper’s PAM platform remains at the cutting edge of security and functionality.
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RESPONSIBILITIES

• Develop and implement machine learning models to enhance threat detection, anomaly detection, and risk assessment in privileged access management (PAM)
• Build scalable pipelines to process and analyze data from Keeper’s ARAM platform, including session recordings, user behavior data, and system access logs
• Design and optimize LLM-based solutions to summarize user activity and extract insights from session recordings and text data
• Fine-tune and monitor models to improve detection accuracy and reduce false positives/negatives
• Implement video/image processing techniques to identify anomalous patterns in session data
• Stay up-to-date with the latest advancements in machine learning, specifically within the security and PAM domains
• Collaborate with security, DevOps, and engineering teams to align ML initiatives with Keeper’s broader security goals
• Write clean, maintainable code and provide comprehensive documentation for models and solutions
• Continuously monitor, troubleshoot, and optimize models in production for scalability and reliability