ML Engineer, Application

About the Company

Dragos is a leading provider of industrial cybersecurity solutions dedicated to safeguarding critical infrastructure and industrial organizations worldwide. With a mission to protect the systems that supply essential services such as water, electricity, and safe working environments, Dragos has established itself as the market leader in ICS/OT cybersecurity. The company offers cutting-edge technology, threat intelligence, and comprehensive services designed to enhance the security and resilience of industrial control systems. Operating as a remote-first organization with a presence across North America, Europe, the Middle East, and the Asia-Pacific region, Dragos values authenticity, transparency, and trust in its culture.

About the Role

We are seeking a Machine Learning Application Engineer to join our dynamic engineering team. This role is positioned at the intersection of data engineering and applied machine learning, focusing on integrating existing ML models into our products and data pipelines. The ideal candidate will not be responsible for training models from scratch or managing ML infrastructure but will instead focus on applying proven techniques to real-world problems, ensuring that the outputs are reliable, interpretable, and actionable. Collaborating closely with AI Engineers, Data Engineers, and product teams, you will help bring ML-driven capabilities such as network behavior clustering, asset classification, and anomaly detection into the Dragos platform. Your work will directly contribute to enhancing cybersecurity defenses for industrial environments, making critical systems more resilient against cyber threats.

Responsibilities

• Apply clustering, classification, anomaly detection, and other established ML techniques to cybersecurity data within the ICS/OT domain
• Integrate ML model outputs into existing data pipelines and product workflows, supporting batch and near-real-time processing
• Analyze and interpret model behavior, translating research outputs into reliable pipeline components
• Collaborate with Data Engineers to ensure ML stages have clear data contracts, proper observability, and fail-safe mechanisms
• Evaluate open-source and third-party models for suitability against specific use cases, determining when to adapt existing tools or develop new models
• Write clean, maintainable code in Python or Rust that can be tested, extended, and understood by other engineers
• Troubleshoot ML components in production environments, diagnosing issues related to output quality, data drift, or edge cases
• Communicate effectively about model functionalities, uncertainties, and appropriate usage of outputs to technical and non-technical audiences

Qualifications

• 4+ years of software engineering experience, with significant exposure to ML outputs or data pipelines in a production setting
• Strong proficiency in Python programming
• Proficiency in SQL and experience working with large-scale data
• Hands-on experience applying machine learning techniques including clustering (k-means, DBSCAN, hierarchical), classification, and anomaly detection
• Familiarity with scikit-learn and the broader Python ML ecosystem
• Understanding of data pipeline concepts, data flow, transformations, failure modes, and observability
• Ability to evaluate the trustworthiness of model outputs for specific use cases
• Excellent written and verbal communication skills, capable of explaining technical concepts to diverse stakeholders
• Experience working in cybersecurity or with threat detection, network behavior, or ICS/OT operations is a plus

Benefits

• Competitive salary package of $225,000.00
• Equity options to share in the company's success
• Comprehensive benefits plan including health, dental, and vision coverage
• Flexible remote work environment supporting work-life balance
• Opportunities for professional growth and development within a pioneering cybersecurity organization
• Collaborative and innovative company culture emphasizing transparency and trust