Senior/Staff/Principal AI/ML Engineer

We're looking for a AI/ML Engineer (Senior/Staff/Principal) - Threat Detection who will design, build, and operationalize the detection algorithms, ML inference pipelines, and risk aggregation systems that power our autonomous threat detection platform. You'll work at the intersection of identity security, behavioral analytics, and applied machine learning building production systems that analyze ZTNA audit logs in near real-time, surface high-fidelity threat signals, and feed into our Risk Sentinel enforcement engine to continuously harden access decisions. See detailed responsibilities at the website for application. Required Qualifications 7+ years of production AI/ML engineering experience, with a strong preference for candidates who have built threat detection, UEBA, ITDR, or identity security platforms at leading security or cloud companies. Detection algorithm expertise: Hands-on experience designing detections for identity-based threats credential compromise, privilege escalation, insider activity, behavioral anomalies, and data exfiltration. MLOps & Productionization: Experience building and operating scalable MLOps platforms for AI/ML systems, including model lifecycle management, CI/CD for ML pipelines, feature stores, automated retraining, model monitoring/drift detection, experiment tracking, and deployment orchestration using Kubernetes, MLflow, Kubeflow, SageMaker, or equivalent tooling in high-throughput production environments. ML proficiency: Experience building AI-powered security systems using large language models, deep learning, and agentic AI techniques for threat detection, anomaly analysis, contextual investigation, and intelligent remediation. Data & streaming engineering: Real-time or near-real-time pipeline experience (Kafka, Flink, Spark Streaming, or equivalent); familiarity with lakehouse formats (Apache Iceberg, Parquet). Security domain knowledge: MITRE ATT&CK, identity threat kill chains, ZTNA or network access control systems, and audit log analysis. Bonus: Experience with detection-as-code frameworks (Sigma, YARA), ZTNA platforms, LLMs or GNNs applied to security, or publications at USENIX, CCS, NeurIPS, or ICML. Mindset: Mission-driven, production-focused, signal-obsessed. You measure precision and recall, you eliminate alert fatigue, and you care that your work protects real systems. Preferred Qualifications - Self-managed data infrastructure: experience operating Kafka, Spark, Elasticsearch, or comparable stateful systems in production. - AI/ML infrastructure: experience building or operating model serving, inference pipelines and MLOps tooling such as MLflow, Kubeflow, SageMaker or equivalent, including model deployment, versioning and drift monitoring. - Networking & Zero Trust fundamentals: working knowledge of the network and routing layer beneath modern access solutions TCP/IP, TLS, tunneling/overlay networks, packet routing and filtering, DNS and firewalling and familiarity with Zero Trust Network Access (ZTNA) or adjacent domains (VPN, SDP, SASE, software-defined networking). You can reason about traffic paths, latency and throughput end-to-end, and instrument the network as a first-class observability signal. Compensation $180K-$275K/year https://apply.workable.com/appgate/j/53EA1923CA/