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 seeking an experienced Staff Machine Learning Engineer to join our Engineering team. In this role, you'll drive the design and implementation of production machine learning systems within the Dragos platform. Working closely with Data Engineers and product teams, you'll build and deploy AI/ML capabilities that enhance threat detection, automate security analysis, and deliver actionable intelligence for Industrial Control System (ICS) and Extended Operational Technology (xOT) cybersecurity applications.  Responsibilities:  - Design and implement production-grade machine learning systems that expand Dragos product capabilities, with consideration for both cloud and resource-constrained on-premises environments.  - Build and optimize ML model architectures for ICS/xOT cybersecurity use cases, including threat detection, asset classification, behavioral analysis, anomaly detection, and natural language processing systems. - Develop robust data pipelines and ML workflows that integrate with existing data infrastructure, supporting both real-time and batch processing requirements. - Collaborate with OT detection experts to translate research concepts and prototypes into scalable, production-ready ML systems. - Partner with Data Engineers to establish data contracts and implement observability frameworks for ML pipelines, including monitoring, versioning, and deployment best practices. - Contribute to ML infrastructure improvements, including automated testing frameworks, CI/CD pipelines, and deployment strategies for containerized environments (Kubernetes, Docker). - Evaluate and adapt state-of-the-art ML research and open-source models to domain-specific cybersecurity applications. - Troubleshoot and optimize ML model performance in production environments, addressing issues related to latency, accuracy, and resource utilization.  Qualifications:  - 6+ years of engineering experience with at least 4 years focused on machine learning implementations in production environments. - Strong software engineering foundation with expertise in Python and SQL as well as experience with at least one additional language (Go, Rust, Java, or JVM-family languages). - Demonstrated experience building and deploying ML systems using modern frameworks and libraries (scikit-learn, PyTorch, TensorFlow, HuggingFace, or similar). - Experience with LLMs, retrieval-augmented generation (RAG), or advanced NLP techniques is beneficial. - Proven track record implementing ML solutions such as classification systems, time series analysis, anomaly detection, or NLP applications that deliver measurable business impact. - Experience with MLOps practices, including model versioning, monitoring, pipeline orchestration, and deployment in high-reliability environments. - Familiarity with data engineering concepts, including data pipelines, stream processing, message queuing, and working with medium-to-large scale datasets. - Knowledge of containerized deployment solutions and cloud-native architectures. - Strong communication skills with the ability to explain technical concepts to diverse stakeholders and collaborate effectively across teams. - Cybersecurity domain knowledge, particularly in threat detection, threat intelligence, or ICS/OT operations. Compensation: - Salary: $190,000.00 - Competitive Equity Package - Comprehensive Benefits Plan #LI-JF1 #LI-REMOTE #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.

• Design and operate the pipelines that give Vibe a coherent view of users across every data source. • Own and improve the clustering algorithm that turns raw events into coherent entities like users and households. • Anticipate scaling pain before it becomes an incident, as log volumes and advertiser usage keep growing. • Run projects end-to-end, from stakeholder requirements through design, implementation, and post-launch iteration. • Build platform-level identity-resolution tooling so every team can resolve identity for the events they own. • Choose architectures that scale economically and quantify the cost impact of those choices. • Shape and execute the roadmap for Vibe's identity stack. • Surface tradeoffs, risks, and decisions to stakeholders without burying them in implementation detail. • Translate ambiguous requests from Product, Sales, ML, and Finance into concrete deliverables.

Role Description We're seeking an experienced Staff Machine Learning Engineer to join our Engineering team. In this role, you'll drive the design and implementation of production machine learning systems within the Dragos platform. Working closely with Data Engineers and product teams, you'll build and deploy AI/ML capabilities that enhance threat detection, automate security analysis, and deliver actionable intelligence for Industrial Control System (ICS) and Extended Operational Technology (xOT) cybersecurity applications. Responsibilities: - Design and implement production-grade machine learning systems that expand Dragos product capabilities, with consideration for both cloud and resource-constrained on-premises environments. - Build and optimize ML model architectures for ICS/OT cybersecurity use cases, including threat detection, asset classification, behavioral analysis, anomaly detection, and natural language processing systems. - Develop robust data pipelines and ML workflows that integrate with existing data infrastructure, supporting both real-time and batch processing requirements. - Collaborate with OT detection experts to translate research concepts and prototypes into scalable, production-ready ML systems. - Partner with Data Engineers to establish data contracts and implement observability frameworks for ML pipelines, including monitoring, versioning, and deployment best practices. - Contribute to ML infrastructure improvements, including automated testing frameworks, CI/CD pipelines, and deployment strategies for containerized environments (Kubernetes, Docker). - Evaluate and adapt state-of-the-art ML research and open-source models to domain-specific cybersecurity applications. - Troubleshoot and optimize ML model performance in production environments, addressing issues related to latency, accuracy, and resource utilization. Qualifications - 6+ years of engineering experience with at least 4 years focused on machine learning implementations in production environments. - Strong software engineering foundation with expertise in Python and SQL as well as experience with at least one additional language (Go, Rust, Java, or JVM-family languages). - Demonstrated experience building and deploying ML systems using modern frameworks and libraries (scikit-learn, PyTorch, TensorFlow, HuggingFace, or similar). - Experience with LLMs, retrieval-augmented generation (RAG), or advanced NLP techniques is beneficial. - Proven track record implementing ML solutions such as classification systems, time series analysis, anomaly detection, or NLP applications that deliver measurable business impact. - Experience with MLOps practices, including model versioning, monitoring, pipeline orchestration, and deployment in high-reliability environments. - Familiarity with data engineering concepts, including data pipelines, stream processing, message queuing, and working with medium-to-large scale datasets. - Knowledge of containerized deployment solutions and cloud-native architectures. - Strong communication skills with the ability to explain technical concepts to diverse stakeholders and collaborate effectively across teams. - Cybersecurity domain knowledge, particularly in threat detection, threat intelligence, or ICS/OT operations. Requirements - Salary: $225,000.00 - Competitive Equity Package - Comprehensive Benefits Plan Company Description 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.

• Lead the Fraud & Risk Machine Learning organization, managing a team responsible for production fraud detection models. • Define and execute the machine learning roadmap for fraud prevention, identity verification, and risk decisioning. • Build and scale a portfolio of production ML models from concept through deployment and continuous optimization. • Partner with Product, Engineering, Risk, and Executive Leadership to solve complex business challenges using machine learning. • Drive end-to-end machine learning development including: Feature engineering, Data preparation, Model development, Validation, Production deployment, Monitoring and model performance optimization. • Establish best practices for model governance, experimentation, and production reliability. • Mentor and grow a high-performing team of Data Scientists and Machine Learning Engineers. • Provide technical leadership while remaining capable of contributing hands-on when necessary. • Present technical strategy, business impact, and model performance to executive stakeholders.