Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. We are looking for a Security Operations Lead (SOC Lead) to build, mature, and operate our 24/7 detection and response capabilities across a modern cloud-native and AI-driven environment. This role leads the global SOC function—monitoring, SIEM ownership, detection engineering, alert triage, and operational readiness—while also evaluating and integrating emerging AI-based SOC products and autonomous response platforms
Задачи
SOC LEADERSHIP & 24/7 MONITORING
Lead, mentor, and scale a global SOC team responsible for 24/7 monitoring, alert intake, triage, correlation, and escalation
Build operational rigor: processes, runbooks, SLAs, metrics, and quality standards for high-scale environments
Endpoints (macOS, Linux, Windows) including EDR/XDR telemetry
Developer platforms + CI/CD pipelines
AI/ML systems and model-serving workflows
AI-BASED SOC INTEGRATION & INNOVATION
Evaluate, adopt, and integrate AI-native SOC technologies for triaging, detection, and correlation
Identify opportunities to automate triage, investigations, enrichment, and reporting
Serve as the internal expert on the capabilities and limitations of AI-based SOC tooling
SIEM & TELEMETRY OWNERSHIP
Own the entire SIEM ecosystem—ingestion, normalization, correlation, enrichment, tuning, dashboards, and metrics
Expand telemetry across
Cloud logs, API logs, system events
SaaS audit logs and admin events
Identity providers (Okta, Google, Azure AD)
Endpoint EDR/XDR event streams
Standardize data schemas and improve detection signal quality across sources
DETECTION ENGINEERING
Develop high-fidelity detections for
Cloud-native attacks
Требования
This is a hands-on leadership role perfect for someone who wants to shape the SOC of the future while solving complex challenges in a high-scale AI setting
Будет плюсом
Experience with UBA/UEBA, ML-driven anomaly detection, or autonomous remediation systems