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Detection Engineer II

AI Summary
Instacart seeks a Detection Engineer II to develop, tune, document, and operate security detections across endpoint, cloud, container, and SaaS telemetry. The role covers the full detection lifecycle, including forensic investigation support, telemetry quality optimization, threat hunting, and SOAR-based response automation. The engineer will use detection-as-code practices such as version control, automated testing, peer review, and CI/CD deployment. Strong knowledge of attacker TTPs, zero-trust identity threats, macOS telemetry, and at least one major cloud platform is required. This is a US-remote, full-time security engineering position that collaborates closely with incident response, red team, engineering, fraud, and trust-and-safety partners.
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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Opportunity details

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

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role requires practical detection-engineering expertise across several complex environments, including macOS, public cloud, containers, and SaaS. It also demands independent technical judgment to translate evolving adversary behavior into reliable, low-noise detections and automated response workflows.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$161,250
US market range (per year)$145k–$185k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses US base-pay ranges of $142,000–$180,500 USD, varying by permanent work location. The annual cadence is inferred from the scale and context of a full-time US security engineering base salary, rather than explicitly stated. Estimated US market base pay for a Detection Engineer II is $145,000–$185,000 USD per year; equity and annual refresh grants are separate from base pay.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you turn a newly observed identity-token theft technique into a production detection?

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.

How do you reduce alert fatigue without creating meaningful detection gaps?

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.

What macOS telemetry would you prioritize for threat detection?

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.

Describe an effective SOAR workflow for a suspicious cloud-account event.

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.

How would you optimize log ingestion while maintaining strong detection coverage?

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.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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