Staff Information Security Engineer Detection Engineering

At LinkedIn our approach to flexible work is centered on trust and optimized for culture connection clarity and the evolving needs of our business. The work location of this role is hybrid meaning it will be performed both from home and from a LinkedIn office on select days as determined by the business needs of the team.

This role will be hybrid in LinkedIns Mountain View office location.

About the Team

LinkedIns Information Security organization protects our members data and infrastructure by building robust defenses detecting attacker activity early and partnering across engineering to  reduce risk.

The Detection Engineering team within InfoSec automates identification and contextualization of attacker activities at LinkedIn and partners across Incident Response Threat Intel Red/Purple Team Product Security IAM and Cloud to develop and maintain high-quality detections. The team also supports log ingestion schema design and normalization incident support tooling and automation threat hunting and audit assistance.

About the role

As a Staff Security Engineer in Detection Engineering you will architect build and operate high-signal detections across endpoint identity cloud and SaaS. Youll drive strategy and technical design through hands-on implementationsdetections-as-code telemetry modeling and rigorous efficacy metrics (signal-to-noise precision/recall latency). Youll lead purple-team validation and adversary emulation to turn TTP-driven hypotheses into resilient low-noise production detections and you mentor peers via standards and code reviews. This is a hands-on staff-level role owning detection strategy architecture and mentoring.
 

Responsibilities

• Define detection strategy and roadmap; drive coverage across priority threat scenarios and emerging attacks relevant to LinkedIn

• Partner with IR/Threat Intel/Cloud/IAM to turn hypotheses and TTPs into production detections; lead purple-team validation.

• Design detections-as-code with version control CI/CD unit/integration tests and staged rollouts.

• Lead adversary emulation exercises to validate detection coverage; develop synthetic signal and test harnesses.

• Proactive threat hunting to discover unknown attacker activity; design hunt playbooks and convert findings into detections.

• Build IR automation (SOAR/Logic Apps) to orchestrate triage enrichment containment and case workflow.

• Operationalize threat intelligence: ingest/normalize IOCs/TTPs enrich detections with TI context and collaborate with TI to turn reports into testable hypotheses.

• Build and maintain a SIGMA-based detection content library; translate SIGMA to KQL/SQL where applicable.

• Own telemetry quality: schemas enrichment normalization and data reliability SLIs/SLOs.

• Establish and monitor detection quality metrics (signal-to-noise ratio precision/recall false-positive rate alert latency lift); drive continuous tuning.

• Lead incident retros to add resilient post-incident detections and suppress noisy patterns.

• Mentor engineers; establish standards code reviews and guidance for detection engineering best practices.

• Participate in on-call for critical detection pipelines and high-severity investigations.

Qualifications :

Basic Qualifications

• BA/BS Degree in CyberSecurity Information Security Computer Science or related technical discipline or related practical experience. 

• 5 years in security engineering detection engineering or incident response

• 2 years technical leadership.

• Expertise with log analytics and detection content for SIEM/XDR/EDR and cloud provider telemetry (AWS/Azure/GCP).

• Experience building detections and automation with scripting languages (e.g. Python) and query languages (e.g. KQL/SQL)

• Experience building detections-as-code (tests CI/CD canary deploys rollback) at large scale.

• Experience with attacker TTPs and frameworks (ATT&CK) and detection efficacy metrics.

• Experience designing schemas and data models (e.g. ASIM/OSSEM-like) and telemetry pipelines.

• Experience with SIGMA rule authoring and translation; adversary emulation/purple-team experience.

Preferred qualifications

• BS and 8 years of relevant work experience MS and 7 years of relevant work experience or PhD and 4 years of relevant work experience. 

• 8 years of experience in detection engineering with 3 years of experience in a technical leadership role

• Rigorous approach to detection quality (SNR precision/recall false-positive rate latency) and measurement.

• Experience operating detections over billions of events/day and multi-region data pipelines.

• Building detection testing harnesses synthetic signal and adversary emulation at scale.

• Familiarity with identity/security signals (AAD/Okta/SSO) endpoint internals (Windows/Linux/macOS) and SaaS logs.

• Applied analytics/ML for anomaly detection risk scoring or enrichment (with robust evaluation).

• Experience building hypotheses and content for AI-enabled attack patterns; practical use of AI to improve detection engineering workflows.

• Relevant certifications (e.g. GCTI GCDA GCFA GIAC-blue); publications or open-source contributions in detections.

Suggested Skills:

• Detection Engineering

• Technical Leadership

• KQL/SQL

• Detection-as-code

You will Benefit from our Culture

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices.

The pay range for this role is $156000 to $255000. Actual compensation packages are based on several factors that are unique to each candidate including but not limited to skill set depth of experience certifications and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus stock benefits and/or other applicable incentive compensation plans. For more information visit Information :

Equal Opportunity Statement 

We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race color religion creed gender national origin age disability veteran status marital status pregnancy sex gender expression or identity sexual orientation citizenship or any other legally protected class.

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No

Employment Type :

Full-time