Senior Machine Learning Engineer, Cybersecurity / Threat Detection

United StatesHybrid

Responsibilities

Design, curate, and maintain datasets for training and evaluating threat detection models
Build custom ML models for domain-specific threat classification and risk assessment
Engineer and optimize prompts for vision-language models to analyze session behavior
Create evaluation frameworks and benchmarks to measure accuracy, robustness, and reliability
Develop Python-based inference services within Dockerized environments
Integrate AI/ML capabilities with WebSocket, WebRTC, and low-level system interfaces for real-time analysis
Write clean, maintainable code and produce clear technical documentation
Monitor, troubleshoot, and optimize models in production for performance, scalability, and reliability Requirements
Manage interviews and recruitment workflow
Lodge a complaint with your data protection authority

We are seeking a highly motivated and experienced Machine Learning Engineer to join our AI & Threat Analytics team.
Join one of the fastest-growing cybersecurity companies and play a critical part in advancing Keeper’s AI-driven threat detection capabilities for our Privileged Access Management (PAM) platform. About Keeper
Privileged accounts are prime targets for attackers, and the ML systems you build will serve as a first line of defense against anomalous and malicious behavior across SSH, RDP, VNC, and database connections.
You will work in a Python-based environment processing real-time session data via WebSocket, WebRTC, and protocol-level interfaces.
experience in machine learning research or development
Strong proficiency in Python Hands-on
experience with dataset collection, curation, and labeling for ML training •
Experience working with vision-language models or large language models (e.g., GPT, Claude, Gemini, Qwen)
Familiarity with prompt engineering techniques and LLM frameworks •
Experience building and deploying ML inference systems using Docker
Working knowledge of graph data structures and their practical applications
Familiarity with Git-based workflows and model repositories (e.g., Hugging Face) •