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About Nscale Nscale is the GPU cloud engineered for AI. We provide cost-effective, high-performance infrastructure for AI start-ups and large enterprise customers. Nscale enables AI-focused companies to achieve superior results by reducing the complexity of AI development. Our GPU cloud bolsters technical capabilities and directly supports strategic business outcomes, including cost management, rapid innovation, and environmental responsibility. We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As an Nscaler, you’ll build trust through openness and transparency, where everyone is inspired to do their best work. If you join our team, you’ll be contributing to building the technology that powers the future. About the Role We are hiring a Senior Staff Engineer - Security Data, Detection and Automation to build the telemetry, detection, response automation, case-quality metrics, and reporting foundation for an increasingly Nscale-owned SOC capability. This role sits at the intersection of security engineering, data engineering, detection engineering, and security operations . You will work across endpoint, identity, SaaS, cloud, network, vulnerability, and production access domains, partnering closely with security leadership as well as adjacent teams shaping identity and vulnerability management requirements. Your impact will be strategic and measurable. The focus is not to create more alerts , but to turn raw telemetry into reliable, explainable, high-signal security outcomes that strengthen internal ownership of detection logic, containment decisions, runbooks, executive metrics, case quality, and automation. What you'll be doing Security Telemetry and Data Foundations Design security telemetry architecture across endpoint security, security analytics, identity platforms, SaaS systems, cloud platforms, vulnerability tools, endpoint inventory, and production access systems. Build a telemetry source map covering ownership, data quality, retention, coverage, priority use cases, and known gaps. Establish data quality, parser quality, ingestion health, field normalization, and source ownership standards. Create daily source-health reporting and scoring for SIEM or security analytics data quality. Detection Engineering and Threat Coverage Own the detection engineering lifecycle from hypothesis and data source selection through logic, testing, tuning, ownership, runbook, expiry, and metrics. Define high-value detection use cases across identity, endpoint, SaaS, cloud, and production access. Develop detections with documented test logic, runbooks, data dependencies, and case-quality criteria. Apply TTP-led threat modeling across corporate, cloud, production, identity, SaaS, endpoint, insider, and AI-agent risk scenarios. Validate detection coverage through attack simulation or other coverage-testing approaches. Automation and Operational Improvement Build SOAR and automation workflows that enrich alerts, suppress low-value noise, route cases, and improve analyst decision-making. Design scalable data pipelines, enrichment flows, and automations that improve operational quality. Implement detection-as-code or version-controlled detection content where practical. Use automation to improve the consistency, explainability, and actionability of security outcomes. SOC Performance and Reporting Measure MDR/SOC performance using case-quality metrics such as false positive rate, time to triage, time to containment, evidence completeness, and escalation quality. Create a MDR/SOC case-quality review loop for internal and external stakeholders. Produce security dashboards and executive reporting that connect security operations to measurable risk reduction. Improve alert explainability so analysts and l