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.
What We Need to See
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.
Ways to Stand Out From the Crowd
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.
- Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
- 168,000 USD – 270,250 USD for Level 4; and 196,000 USD – 310,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 8, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
