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Posted Jan 27Senior Machine Learning Engineer, Cybersecurity / Threat Detection
at Keeper.app
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
Requirements
- 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) •