Senior Security Engineer, Detection Engineering

NVIDIA•Us, CA

About The Position

NVIDIA Security is seeking a Senior Detection Engineer to join the Detection and Automation Engineering team. On this team, we build reliable detection coverage that helps responders identify real threats quickly, reduce unnecessary noise, and protect NVIDIA's enterprise, cloud, developer, AI, and production environments. We want someone who can move from hypothesis to tested production content and explain the tradeoffs clearly. We work closely with incident response, threat intelligence, security operations, cloud security, and engineering teams. We protect enterprise, cloud, identity, endpoint, collaboration, developer, AI, and production environments. We care about detections that are explainable, measured, maintainable, and useful during real investigations.

Requirements

8+ years of experience in detection engineering, security engineering, threat hunting, incident response, SOC engineering, or information security monitoring.
A degree in Computer Science, Cybersecurity, Engineering, or equivalent experience
Hands-on experience building and tuning detections in a major SIEM or security analytics platform.
Strong query and scripting skills, especially SPL, KQL, SQL, Python, or similar languages used to analyze security telemetry and automate repetitive work.
Practical understanding of attacker behavior across identity, endpoint, cloud, network, email, collaboration tools, developer infrastructure, secrets, and data theft.
Ability to move from raw logs to a production-ready detection, including field semantics, thresholds, joins, baselines, false positives, missing data, and triage context.
Comfortable working with Git, code review, automated checks, CI/CD, documentation, and operational ownership of the content you ship.
Clear communication and sound judgment. You should be able to explain why a detection matters, what it misses, what we should do next, and when we should choose not to alert.

Nice To Haves

You have led or materially improved a detection-as-code program across more than one security platform.
Background in cloud, identity, Kubernetes, CI/CD, supply-chain, or AI-tooling attack paths well enough to design detections without waiting for a complete recipe.
Detected abuse in developer systems, package ecosystems, secrets workflows, AI coding tools, agents, bots, or model-serving infrastructure.
Worked closely with incident response, threat hunting, red team, purple team, malware analysis, or forensics teams.
Built validation methods such as replay tests, synthetic telemetry, attack emulation, detection unit tests, or coverage reporting.

Responsibilities

Build and tune high-confidence detections across platforms such as Splunk, Microsoft Sentinel / Defender, CrowdStrike, cloud-native logs, identity telemetry, endpoint data, SaaS platforms, and developer systems.
Translate incidents, hunts, threat intelligence, red-team findings, and vulnerability context into detection logic we can test and operate.
Investigate real telemetry before we alert on it, including field behavior, timing, joins, baselines, and the false positives a responder will actually see.
Drive the full detection lifecycle: design, query development, validation, peer review, documentation, deployment, tuning, monitoring, and retirement.
Strengthen detection-as-code workflows, including test data, quality checks, metadata, rollout safety, coverage tracking, and detection health reporting.
Partner with responders to cut noise, add useful context, improve severity decisions, and build follow-up detections after investigations.
Shape logging, normalization, enrichment, and retention requirements when the telemetry we need is missing or hard to use.
Coach analysts and engineers through design reviews, query reviews, and clear guidance on what makes a detection credible.