SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Illumio is seeking a Staff Security ML Researcher to develop machine learning-driven approaches for threat detection, risk assessment, and security analytics within the Office of the CTO and Security team. This role sits at the intersection of cybersecurity, machine learning, and product innovation, partnering with threat researchers, engineers, and product teams to transform large-scale security data into intelligent models and actionable insights.
Key responsibilities include:
**Machine Learning & Security Intelligence:** Design, develop, and deploy ML models that identify anomalous behavior, detect threats, and predict security risk across complex enterprise environments. Apply statistical techniques and predictive modeling to evaluate breach likelihood and attack exposure. Build behavioral profiling, anomaly detection, clustering, classification, and forecasting models using large-scale security telemetry. Evaluate model performance, reduce false positives, and continuously improve detection quality through experimentation.
**Threat Research & Risk Modeling:** Analyze large-scale security datasets to identify attacker behaviors and emerging threats aligned to frameworks like MITRE ATT&CK. Develop threat scoring and risk assessment models to help customers prioritize remediation. Leverage graph-based analysis to model attack paths, quantify lateral movement risk, and recommend mitigation strategies. Work closely with threat researchers to transform security intelligence into measurable detection capabilities.
**Data Science & Analytics Engineering:** Design and maintain scalable data pipelines for model training, validation, and analytics. Investigate new features, signals, and telemetry sources to improve detection accuracy. Partner with engineers to productionize models and analytics systems. Conduct experiments and hypothesis-driven analysis to validate new detection approaches.
**Product Collaboration & Innovation:** Collaborate with product managers, designers, and engineers to embed ML-driven security insights into customer-facing experiences. Influence product strategy through data-backed recommendations. Help define security analytics frameworks and detection methodologies for future capabilities. Serve as a subject matter expert on machine learning in cybersecurity.
**Research & Thought Leadership:** Explore emerging techniques in machine learning, graph analytics, and AI-driven threat detection. Investigate applications of graph neural networks and probabilistic modeling for cyber defense. Contribute to patents, technical publications, and conference presentations. Stay current on advancements in cybersecurity and adversary tradecraft.
The role requires 4 on-site days per week at Sunnyvale headquarters.
**Requirements:**
- 5+ years of experience in security analytics, threat research, detection engineering, data science, or machine learning
- Strong Python programming skills with experience in Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch
- Experience designing, training, validating, and deploying ML or statistical models in production environments
- Proven ability to work with large-scale telemetry datasets from security, networking, cloud, or infrastructure environments
- Strong understanding of model evaluation, feature engineering, experimentation, and handling imbalanced datasets
- Experience applying analytics or ML techniques to cybersecurity problems (anomaly detection, behavioral analysis, threat hunting, risk assessment)
- Familiarity with security frameworks such as MITRE ATT&CK and common security telemetry sources
- Strong SQL and data querying skills
- Excellent communication skills with ability to translate technical findings into actionable business and product recommendations
**Preferred Qualifications:**
- 7-10+ years of experience spanning cybersecurity, machine learning, data science, or threat research
- Experience building graph-based security analytics using Neo4j, graph databases, or graph algorithms
- Knowledge of graph machine learning techniques including graph neural networks or link prediction methods
- Experience productionizing ML models in cloud environments using AWS, Kubernetes, or similar platforms
- Experience developing risk-scoring systems, recommendation engines, or predictive analytics solutions
- MS or PhD in Computer Science, Data Science, Machine Learning, Cybersecurity, Statistics, or related field
- Background at a cybersecurity company focused on cloud security, network security, endpoint security, or threat intelligence
- Experience integrating external threat intelligence feeds into ML workflows
- Publications, patents, open-source contributions, or conference presentations related to security or applied machine learning
- Industry certifications such as CISSP, GIAC, or advanced machine learning credentials
