Dragos

Staff ML Application Engineer

United States · Staff+

Sponsorship not specifiedDetected 13 days ago

PythonRustSQLDockerKubernetesMachine Learningscikit-learnData EngineeringCybersecurityDetection EngineeringResearchCommunication

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.

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.
The systems we protect deliver the water you drink, power your home, and keep the hospitals your community depends on running.

4+ years of software engineering experience, with meaningful time spent working with ML outputs or data pipelines in a production context.
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
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.
Strong Python skills
SQL proficiency
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.

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.
All new hires must pass a background check as a condition of employment.
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.
Cybersecurity domain knowledge - especially around threat detection, network behavior, or ICS/OT operations is a meaningful plus, but not a prerequisite.

We are the global leader in xOT cybersecurity, combining technology, threat intelligence, and expert services.

This listing is sourced directly from Dragos's careers page and normalized into a canonical job model.