Machine Learning Engineer, PEDM

Position Overview

• Location Type: Remote (Hybrid option for candidates in El Dorado Hills, CA or Chicago, IL metro area)
• Job Type: Full-time
• Salary: Not specified

Keeper Security is a leading cybersecurity company trusted by millions globally. We are seeking a highly motivated and experienced Machine Learning Engineer to join our AI & Threat Analytics team. This role offers the opportunity to work on next-generation autofill and classification models within our browser extension, contributing to a product used in 21 languages across over 120 countries.

About the Role

This position focuses on shaping and leading innovation in Privilege Elevation and Delegation Management (PEDM). You will design and deploy real-time models to detect, classify, and analyze elevated access events and user behaviors. The role involves working with sequence and behavior modeling, temporal anomaly detection, and GenAI features to provide intelligent access decisions with minimal false positives, enhancing security, visibility, and usability. You will build production-grade, privacy-preserving AI systems at scale, leveraging LLMs, transformers, and time-series models.

Responsibilities

• Build, fine-tune, and deploy models using LLMs, transformers, and sequence architectures (e.g., Llama, Qwen, BERT, LSTM, TCN).
• Build classifiers for behavioral and temporal signals from user and system activity data.
• Generate and augment datasets using synthetic, multilingual data and edge-case coverage.
• Scale ML experimentation and deployment pipelines with MLOps best practices.
• Create systems targeting cloud, on-prem, and self-hosted environments for client-side inference.
• Continuously improve model performance, latency, and reliability.
• Implement secure, privacy-aware inference strategies aligned with zero-trust principles.
• Collaborate with cross-functional teams to align ML efforts with product goals.
• Write clean, maintainable code and provide comprehensive documentation.

Requirements

• 5+ years of professional experience in ML engineering or research, with a focus on cybersecurity, NLP, or sequence/temporal modeling.
• Strong coding skills in Python, JavaScript (React), or a similar language relevant to ML.
• Hands-on experience using and fine-tuning large language models, embeddings, or multilingual models for real-world cybersecurity tasks.
• Proficiency with ML frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
• Familiarity with MLOps, model deployment, and monitoring practices.
• Experience with model validation and metrics (precision, recall, F1-score).
• Familiarity with cloud platforms (AWS, GCP, Azure).
• Knowledge of secure coding, encryption, zero-trust, and zero-knowledge principles.
• Excellent problem-solving and communication skills.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related discipline, or equivalent experience.
• Important: Due to the role's involvement in GovCloud, all applicants must be a "US Person".

Company Information

Keeper Security is transforming cybersecurity for organizations globally with zero-trust privileged access management built with end-to-end encryption. Our cybersecurity solutions are FedRAMP and StateRAMP Authorized, SOC 2 compliant, FIPS 140-2 validated, as well as ISO 27001, 27017, and 27018 certified. Keeper deploys in minutes, not months, and seamlessly integrates with any tech stack to prevent breaches, reduce help desk costs, and ensure compliance. Trusted by millions of individuals and thousands of organizations, Keeper is the leader for password, passkey, and secrets management, privileged access, secure remote access, and encrypted messaging. Learn more at KeeperSecurity.com.

Benefits

• Not specified