Applied ML Engineer for Cybersecurity Data Pipelines Remote

Dragos, Inc.; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity package

Job summary

Dragos, Inc. is seeking a Machine Learning Application Engineer to join our Engineering team. This role sits at the intersection of data engineering and applied ML, focusing on putting existing model types to work inside product and data pipelines.

You will not train models from scratch, but will determine which techniques fit problems, wire them into workflows, and ensure outputs are reliable. You’ll collaborate with AI Engineers, Data Engineers, and product teams to bring ML-driven

Qualifications

• 5+ years of software engineering experience with ML outputs or data pipelines in production.
• Strong Python skills; SQL proficiency; ability to reason about data at scale.
• Hands-on experience applying ML techniques including clustering, classification, and anomaly detection.

Responsibilities

• Apply clustering, classification, anomaly detection, and other ML techniques to cybersecurity data problems in the IC/S/OT domain.
• Integrate ML outputs into data pipelines and product workflows for batch and near-real-time processing.
• Understand model behavior and translate research outputs into reliable pipeline components.
• Collaborate with Data Engineers to ensure data contracts, observability, and fail-safes in ML-driven pipeline stages.
• Evaluate open-source and third-party models for fit vs. use-case; know when to adapt or build anew.
• Write clean, maintainable Python or Rust code that others can reason about and extend.
• Troubleshoot ML components in production to diagnose output quality issues and data drift.
• Clearly communicate model behavior, uncertainties, and how outputs should be used downstream.

Skills

Python

SQL

ML pipelines

Data engineering

scikit-learn

Data wrangling

Communication

Cybersecurity knowledge

Tools

Docker

Kubernetes

Graph data

Stream processing

Job description

Dragos, Inc. is seeking a Machine Learning Application Engineer to join our Engineering team. This role sits at the intersection of data engineering and applied ML, focusing on putting existing model types to work inside product and data pipelines.

You will not train models from scratch, but will determine which techniques fit problems, wire them into workflows, and ensure outputs are reliable. You’ll collaborate with AI Engineers, Data Engineers, and product teams to bring ML-driven