- Level
- Senior
- Experience
- 8+ years
- Work style
- Remote
- Full-time
- Area
- Defence & SOC
At a glance
The role in plain English
This senior detection engineering role at NVIDIA involves designing, building, and maintaining security detections across a range of enterprise, cloud, and developer environments. The position suits experienced security professionals who are comfortable with SIEM platforms, scripting, and collaborating with incident response and engineering teams. Day-to-day work includes developing detection logic, tuning alerts, reviewing telemetry, and supporting detection-as-code workflows.
What you'll do
- Build and tune detections across SIEM and security platforms
- Translate incidents and threat intelligence into detection logic
- Investigate telemetry to validate and refine alerts
- Drive the full detection lifecycle from design to retirement
- Coach analysts and engineers on detection quality
What they're looking for
Essential
- 8+ years in detection or security engineering roles
- Degree in Computer Science, Cybersecurity, Engineering or equivalent experience
- Hands-on experience with SIEM or security analytics platforms
- Strong query and scripting skills (SPL, KQL, SQL, Python)
- Understanding of attacker behaviour across multiple environments
Nice to have
- Led detection-as-code across multiple platforms
- Expertise in cloud, identity, Kubernetes, or AI-tooling attack paths
- Experience detecting abuse in developer or AI systems
- Built validation methods for detection coverage
Core skills
- Splunk
- Microsoft Sentinel
- CrowdStrike
- Python
- KQL
- Threat hunting
- Detection engineering
- Git
The shape of the job
Technical depth
4/5
Pace
3/5
Ownership
4/5
Teamwork
4/5
Cyberend's reading of the listing, not the employer's own scores.
Application toolkit
Make your application count
See which requirements your CV meets and what is missing.
Your CV is used for this answer only and never stored.
Questions to prepare for
- 01Describe your process for developing a new detection from incident data.
- 02How do you balance detection coverage with minimising false positives?
- 03Explain how you would validate a new detection before deployment.
- 04Give an example of improving detection-as-code workflows in a previous role.
About NVIDIA
NVIDIA Security builds and maintains detection coverage to protect enterprise, cloud, developer, AI, and production environments.
Full job descriptionAs published by NVIDIA
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. Do you enjoy turning sophisticated security signals into detections responders can trust? Can you read an incident write-up, a red-team result, or an unusual authentication pattern and decide what is worth alerting on? 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.
What You'll Be Doing:
- 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.
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.
