Dragos
ICS/OT Cybersecurity
StaffMLApplicationEngineer
Neural analysis suggests this role is
optimal for Staff candidates.
“Staff ML Application Engineer at Dragos. Skills: ML Application Engineering, Data Pipelines, ICS/OT Cybersecurity. Apply ML techniques to cybersecurity data problems. Integrate ML model outputs into data pipelines”
What You'll Achieve.
Make ML outputs reliable; Make ML outputs useful; Bring ML-driven capabilities into the Dragos platform; Surface anomalies that matter for ICS/OT security analysts
Industry & Context.
Figuring out which techniques fit which problems; Troubleshoot ML component behavior in production; Diagnose issues with output quality; Diagnose issues with data drift; Diagnose issues with unexpected edge cases
Pass a background check
What They're Looking For.
Must Have
4+ years of software engineering experience, meaningful time spent working with ML outputs or data pipelines in a production context, Python, SQL comfort reading and reasoning about data at scale, Hands-on experience applying ML techniques including clustering (k-means, DBSCAN, hierarchical), classification, and anomaly detection, Familiarity with scikit-learn, Solid understanding of data pipeline concepts, Ability to evaluate whether a model's outputs are actually trustworthy for a given use case, written and verbal comfortable explaining tradeoffs to both technical and non-technical stakeholders
Nice to Have
Cybersecurity domain knowledge — especially around threat detection, network behavior, or ICS/OT operations, Experience working with graph-based representations of network topology or asset relationships, Familiarity with stream processing or event-driven architectures, Exposure to containerized environments (Docker, Kubernetes) as a consumer/deployer
What You'll Do.
Apply ML techniques to cybersecurity data problems
Integrate ML model outputs into data pipelines
Support batch processing
Support near-real-time processing
Understand model behavior
Translate research outputs into pipeline components
Ensure ML-driven stages have clear data contracts
Ensure ML-driven stages have appropriate observability
Ensure ML-driven stages have sane failure modes
Evaluate open-source models
Evaluate third-party models
maintainable Python or Rust
Troubleshoot ML component behavior in production
Communicate about model behavior
How You'll Work.
Team & Collaboration
Work closely with AI Engineers; Work closely with Data Engineers; Work closely with product teams; Work with Data Engineers to ensure data contracts; Work with Data Engineers to ensure observability; Work with Data Engineers to ensure failure modes; Communicate tradeoffs to technical stakeholders; Communicate tradeoffs to non-technical stakeholders
Communication Scope
Communicate clearly about what a model is doing; Communicate clearly where a model is uncertain; Communicate clearly how model outputs should be used; Communicate clearly how model outputs shouldn't be used
Full Job Description
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 looking for a Machine Learning Application Engineer to join our Engineering team. This role sits at the intersection of data engineering and applied ML. You'll be taking existing model types and putting them to work inside our product and data pipelines. You won't be training models from scratch or managing ML infrastructure, but you will be doing the thoughtful applied work of figuring out which techniques fit which problems, wiring them into our workflows, and making sure the outputs are reliable and useful. You'll work closely with AI Engineers, Data Engineers, and product teams to bring ML-driven capabilities into the Dragos platform. Things like clustering network behaviors, classifying assets, and surfacing anomalies that matter for ICS/OT security analysts. Responsibilities: Apply clustering, classification, anomaly detection, and other established ML techniques to cybersecurity data problems in the ICS/OT domain. Integrate ML model outputs into existing data pipelines and product workflows, supporting both batch and near-real-time processing patterns. Understand model behavior and translate research outputs into reliable pipeline components. Work with Data Engineers to ensure ML-driven stages of the pipeline have clear data
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