Machine Learning Engineer, PAM

El Dorado Hills, California, United States - Remote

Keeper Security, Inc.

Manage credentials, secure sensitive data and stop online threats. Keeper is the top-rated password manager for individuals and Privileged Access Management (PAM) solution for businesses.

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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.

About Keeper

Keeper Security is transforming cybersecurity for people and organizations around the world. Keeper’s affordable and easy-to-use solutions are built on a foundation of zero-trust and zero-knowledge security to protect every user on every device. Our award-winning, zero-trust, privileged access management platform deploys in minutes and seamlessly integrates with any tech stack and identity application to provide visibility, security, control, reporting and compliance across an entire enterprise. Trusted by millions of individuals and thousands of organizations, Keeper is an innovator of best-in-class password management, secrets management, privileged access, secure remote access and encrypted messaging. Learn more at KeeperSecurity.com.

About the Role

We are seeking a highly motivated Machine Learning Engineer to join our AI & Threat Analytics team and focus on Privileged Access Management (PAM). In this role, you will develop machine learning models that help secure and streamline privileged access controls for organizations. You will design, implement, and optimize advanced machine learning algorithms that enhance the security and efficiency of our PAM solutions.

As part of our team, you'll work on developing systems for detecting and analyzing anomalous behavior, improving access control policies, and identifying potential threats to privileged accounts. You’ll collaborate closely with cross-functional teams to ensure that Keeper’s PAM platform remains at the cutting edge of security and functionality.

Responsibilities

  • Develop and implement machine learning models to improve privileged access management, including threat detection, risk assessment, and anomaly detection.
  • Build and optimize feature extraction pipelines to analyze user behavior, system access patterns, and security events.
  • Design and deploy real-time models that enhance the security of privileged access across various environments.
  • Fine-tune models to improve detection accuracy and reduce false positives/negatives in identifying suspicious activities.
  • Continuously monitor and enhance model performance, ensuring scalability and reliability as the platform grows.
  • Stay up-to-date with the latest advancements in machine learning, specifically within the security and PAM domains.
  • Collaborate with security and DevOps teams to align machine learning initiatives with Keeper's security goals.
  • Write clean, maintainable code and provide comprehensive documentation for models and solutions.
  • Troubleshoot and optimize models in production to ensure consistency, performance, and reliability.

Requirements

  • 3+ years of professional experience in machine learning research or development, with a focus on security applications (preferably PAM or similar domains).
  • Strong coding skills in Python, JavaScript, or a similar language relevant to ML development.
  • Hands-on experience with anomaly detection, risk modeling, and behavior analysis in security contexts.
  • Proficiency with machine learning frameworks like TensorFlow, PyTorch, and Hugging Face Transformers.
  • Experience working with privileged access management or cybersecurity-related tools and platforms 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.
  • Familiarity with MLOps, model deployment, and monitoring best practices.
  • 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.

Benefits

  • Medical, Dental & Vision (Inclusive of domestic partnerships)
  • Employer Paid Life Insurance & Employee/Spouse/Child Supplemental life
  • Voluntary Short/Long Term Disability Insurance
  • 401k (Roth/Traditional)
  • A generous PTO plan that celebrates your commitment and seniority (including paid Bereavement/Jury Duty, etc)
  • Above market annual bonuses

Keeper Security, Inc. is an equal opportunity employer and participant in the U.S. Federal 

E-Verify program. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Classification: Exempt



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* Salary range is an estimate based on our InfoSec / Cybersecurity Salary Index 💰

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Tags: Analytics AWS Azure Cloud Compliance Computer Science DevOps GCP JavaScript Machine Learning Monitoring Python Risk assessment Threat detection

Perks/benefits: 401(k) matching Career development Health care Insurance Salary bonus Team events

Regions: Remote/Anywhere North America
Country: United States

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