Senior AI/ML Engineer at Dragos

Job description

At Dragos, the mission is personal. The systems we protect deliver the water you drink, power your home, and keep the hospitals your community depends on running. Those critical infrastructure systems that power our civilization around the world are under attack every day by adversaries. When those systems fail, people are immediately at risk. We are the global leader in xOT cybersecurity, combining technology, threat intelligence, and expert services. The people here chose this work because they understand what is at stake . Here, you will find a remote-first mission-driven team across North America, Europe, the Middle East, and APAC built on authenticity, transparency, and trust. If safeguarding the systems that protect your family, friends, and community is the kind of work that matters to you, you are in the right place. 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 contracts, appropriate observability, and sane failure modes. Evaluate open-source and third-party models for fit against specific use cases, knowing when to apply an existing tool versus when to escalate to a model-building effort. Write clean, maintainable Python or Rust that other engineers can reason about, test, and extend. Troubleshoot ML component behavior in production to diagnose issues with output quality, data drift, or unexpected edge cases. Communicate clearly about what a model is doing, where it's uncertain, and how its outputs should (and shouldn't) be used downstream. Qualifications 5+ years of software engineering experience, with meaningful time spent working with ML outputs or data pipelines in a production context. Strong Python skills; SQL proficiency; 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 and the surrounding Python ML ecosystem; you don't need to have implemented a neural net, but you should know how to use one responsibly. Solid understanding of data pipeline concepts: how data flows, where it gets transformed, what can go wrong, and how to make failures visible. Ability to evaluate whether a model's outputs are actually trustworthy for a given use case — not just whether accuracy metrics look good. Strong written and verbal communication; 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 is a meaningful plus, but not a prerequisite. 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, not necessarily an operator. Compensation : Salary: $190,000 Competitive Equity Package Comprehensive Benefits Plan #LI-NH1 #LI-REMOTE Dragos is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics, or any other basis forbidden under federal, state, or local laws. All new hires must pass a background check as a condition of employment.

Requirements

Must-haves

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 contracts, appropriate observability, and sane failure modes.
Evaluate open-source and third-party models for fit against specific use cases, knowing when to apply an existing tool versus when to escalate to a model-building effort.
Write clean, maintainable Python or Rust that other engineers can reason about, test, and extend.
Troubleshoot ML component behavior in production to diagnose issues with output quality, data drift, or unexpected edge cases.
Communicate clearly about what a model is doing, where it's uncertain, and how its outputs should (and shouldn't) be used downstream.
5+ years of software engineering experience, with meaningful time spent working with ML outputs or data pipelines in a production context.
Strong Python skills; SQL proficiency; comfort reading and reasoning about data at scale.
Solid understanding of data pipeline concepts: how data flows, where it gets transformed, what can go wrong, and how to make failures visible.
Ability to evaluate whether a model's outputs are actually trustworthy for a given use case — not just whether accuracy metrics look good.
Strong written and verbal communication; comfortable explaining tradeoffs to both technical and non-technical stakeholders.

Nice-to-haves

Cybersecurity domain knowledge — especially around threat detection, network behavior, or ICS/OT operations is a meaningful plus, but not a prerequisite.
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, not necessarily an operator.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaipythonrustsqlgodockerkubernetes

Platform: greenhouse
Effort: quick