Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Detection Engineer II
- Remote from
- USA
- Salary
- USD 142k–180,500 / yr
- Department
- Cybersecurity
- Employment
- Full Time
- Experience
- Open level
- Published
- Apply before
- 9 Dec 2026
- Listing views
- 29
- Application actions
- 3
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About this role.
We’re transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.
Overview
Instacart’s Detection Engineering team sits at the core of our Security organization, building and operating the systems that identify, surface, and respond to threats across one of North America’s largest grocery technology platforms. We own the full detection lifecycle, from telemetry collection and signal design to automated response, across a complex, cloud-native environment spanning endpoint, cloud, container, and SaaS.
As a Detection Engineer, you’ll be a technical anchor on the team: developing high-fidelity detection logic, hunting for novel attacker techniques, and raising the bar for how we think about coverage, quality, and scale. You’ll work closely with Engineering, Red Team, Incident Response, Fraud, and Trust & Safety to ensure our detections reflect real-world adversary behavior (and not just signatures).
We operate with a detection-as-code mindset: everything we build is versioned, tested, and deployed through repeatable pipelines. We care deeply about reducing noise, improving analyst efficiency through automation and SOAR, and continuously evolving our coverage as the threat landscape shifts.
If you’re energized by hard forensic problems, enjoy translating attacker TTPs into durable detection logic, and want to help shape the future of a growing security function, this role is for you.
About the Job
- Develop, tune, document, and maintain detection logic across multiple log sources including endpoint, cloud, container, and SaaS products.
- Assist in cyber forensic investigations across a variety of log sources
- Optimize log ingestion pipelines and telemetry collection to ensure high-quality, actionable security data while managing volume and cost
- Design and build SOAR playbooks and automation workflows to streamline detection triage, enrichment, and response actions
- Mentor/knowledge share with other detection engineers on threat hunting methodologies, detection logic development, and investigation techniques
About You
Minimum Qualifications
- 2+ years of experience in a detection engineering, incident response, or offensive security role.
- Experience with 1 or more public cloud platforms (AWS, Azure, GCP)
- Deep understanding of attacker TTPs across modern zero trust environments, including identity compromise, token theft, and abuse of trust boundaries
- Proficient understanding of macOS internals and telemetry available to identify macOS specific threats
- Experience implementing detection-as-code workflows including version control, peer review processes, automated testing, and CI/CD deployment pipelines
- Basic proficiency with Python, Golang, or other programming/scripting languages
- Relevant certifications: GCFA, GCFE, GNFA, GREM, OSCP, GCIA, or similar
Preferred Qualifications
- Background in offensive security or red teaming
- Knowledge of machine learning for threat detection
#LI-remote
Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.
For US based candidates, the base pay ranges for a successful candidate are listed below.
CA, NY, CT, NJ
$171,000—$180,500 USD
WA
$164,000—$173,000 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI
$157,000—$165,500 USD
All other states
$142,000—$150,000 USD
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Sample interview questions
I would first map the technique to its relevant ATT&CK behaviors and identify the telemetry needed, such as identity-provider, endpoint, cloud audit, and session logs. I would develop and test an initial analytic against representative benign and malicious activity, add context such as unusual device, IP, session, or privilege signals, then deploy it through the detection-as-code pipeline. After release, I would measure fidelity, tune exclusions carefully, document response steps, and iterate based on analyst feedback.
I begin by measuring alert volume, true-positive rate, time to triage, and common benign causes. I improve fidelity through entity context, behavioral sequences, risk scoring, deduplication, and automated enrichment rather than broadly suppressing activity. Any tuning change should be peer-reviewed, tested against known attack cases, and monitored after deployment to ensure coverage is retained.
I would prioritize process execution and ancestry, code-signing and notarization status, persistence mechanisms, launch agents and daemons, shell history where available, network connections, file-system events, TCC-related access, and endpoint security framework telemetry. I would use this data to identify behaviors such as unsigned execution, suspicious child processes, credential access attempts, persistence creation, and unusual outbound connections.
A useful playbook would enrich the alert with identity, MFA, recent login, device, IP reputation, geolocation, cloud audit activity, and privilege-change context. Based on a risk threshold, it could open a case, notify the on-call analyst, revoke sessions or keys, require credential reset, and preserve relevant logs for investigation. Automated containment should include approvals or well-defined safeguards for high-impact actions.
I would inventory telemetry by detection value, cost, volume, retention needs, and data quality. High-value security events should remain available with normalization and integrity checks, while noisy low-value data can be filtered, sampled only where appropriate, or retained for shorter periods. I would validate every ingestion change against detection dependencies and use coverage metrics to confirm that essential signals remain intact.
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