ML Engineer, Behavioral Analytics

We are looking for a Machine Learning Engineer to develop and deploy models for user and entity behavior analytics (UEBA). You will build anomaly detection systems that identify insider threats, compromised accounts, and lateral movement with minimal false positives.

Our behavioral analytics engine processes billions of authentication events, network flows, and application logs to build baseline profiles for every user and entity in a customer environment. Your models will detect deviations from these baselines that indicate potential security incidents.

This is a unique opportunity to apply machine learning to one of the hardest problems in cybersecurity. You will work with large-scale real-world security data, collaborate with domain experts, and see your models directly protect organizations from advanced threats.

Responsibilities

• ▸ Develop and train ML models for anomaly detection on user and entity behavior
• ▸ Build feature engineering pipelines for security telemetry data at scale
• ▸ Design and implement model serving infrastructure for real-time inference
• ▸ Collaborate with threat researchers to incorporate domain knowledge into model design
• ▸ Conduct rigorous model evaluation with focus on false positive reduction
• ▸ Monitor model performance in production and implement automated retraining pipelines

Requirements

• ▸ 3+ years of experience in applied machine learning or data science
• ▸ Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or scikit-learn)
• ▸ Experience with anomaly detection, time-series analysis, or unsupervised learning
• ▸ Familiarity with ML infrastructure (MLflow, Kubeflow, or similar)
• ▸ Understanding of distributed data processing (Spark, Flink, or Dask)
• ▸ Ability to communicate complex technical concepts to non-ML stakeholders

Nice to Have

• ▸ Domain experience in cybersecurity, fraud detection, or insider threat analytics
• ▸ Published research in anomaly detection or behavioral analytics
• ▸ Experience with graph neural networks or knowledge graph approaches
• ▸ Background in streaming ML or online learning systems

Interested in this role?

Send us your resume and a brief note about why you are excited about this position. We review every application and respond within five business days.