Staff Machine Learning Engineer

Dragos is on a relentless mission to defend industrial organizations that provide us with the necessities of modern civilization. We're seeking an experienced Staff Machine Learning Engineer to join our Engineering team, where you'll drive the design and implementation of production machine learning systems to enhance threat detection and automate security analysis for industrial control systems.

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

Requirements:

• 6+ years of engineering experience with at least 4 years focused on machine learning implementations in production environments
• Strong software engineering foundation with expertise in Python and SQL as well as experience with at least one additional language (Go, Rust, Java, or JVM-family languages)
• Demonstrated experience building and deploying ML systems using modern frameworks and libraries (scikit-learn, PyTorch, TensorFlow, HuggingFace, or similar)
• Proven track record implementing ML solutions such as classification systems, time series analysis, anomaly detection, or NLP applications that deliver measurable business impact
• Experience with MLOps practices, including model versioning, monitoring, pipeline orchestration, and deployment in high-reliability environments
• Familiarity with data engineering concepts, including data pipelines, stream processing, message queuing, and working with medium-to-large scale datasets
• Knowledge of containerized deployment solutions and cloud-native architectures
• Strong communication skills with the ability to explain technical concepts to diverse stakeholders and collaborate effectively across teams
• Experience with LLMs, retrieval-augmented generation (RAG), or advanced NLP techniques is beneficial
• Cybersecurity domain knowledge, particularly in threat detection, threat intelligence, or ICS/OT operations, is a strong plus