Staff Machine Learning Engineer
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About the Role
Dragos is on a relentless mission to defend industrial organizations that provide us with the necessities of modern civilization; running water, functioning electricity, and safe industrial working environments. As the market leader in ICS/OT Cybersecurity, we are dedicated to arming our customers with best-in-class technology, threat intelligence, and services to protect their systems as effectively and efficiently as possible. We’re a remote-first culture with operations in North America, Europe, the Middle East, and APAC. We’re looking for mission-oriented teammates who embody our core values of authenticity, transparency, and trust. Are you ready to make a difference? Come join a mission that can save the world!
About the Role:
We're seeking an experienced Staff Machine Learning Engineer to join our Engineering team. In this role, you'll drive the design and implementation of production machine learning systems within the Dragos platform. Working closely with Data Scientists, Data Engineers, and product teams, you'll build and deploy AI/ML capabilities that enhance threat detection, automate security analysis, and deliver actionable intelligence for Industrial Control System (ICS) and Operational Technology (OT) cybersecurity applications.
Responsibilities:
- Design and implement production-grade machine learning systems that expand Dragos product capabilities, with consideration for both cloud and resource-constrained on-premises environments.
- Build and optimize ML model architectures for ICS/OT cybersecurity use cases, including threat detection, asset classification, behavioral analysis, anomaly detection, and natural language processing systems.
- Develop robust data pipelines and ML workflows that integrate with existing data infrastructure, supporting both real-time and batch processing requirements.
- Collaborate with Data Scientists to translate research concepts and prototypes into scalable, production-ready ML systems.
- Partner with Data Engineers to establish data contracts and implement observability frameworks for ML pipelines, including monitoring, versioning, and deployment best practices.
- Contribute to ML infrastructure improvements, including automated testing frameworks, CI/CD pipelines, and deployment strategies for containerized environments (Kubernetes, Docker).
- Evaluate and adapt state-of-the-art ML research and open-source models to domain-specific cybersecurity applications.
- Troubleshoot and optimize ML model performance in production environments, addressing issues related to latency, accuracy, and resource utilization.
Qualifications:
- 6+ years of engineering experience with at least 4 years focused on machine learning implementations in production environments.
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