Cybersecurity RL Environment Reviewer — Security Engineering (Defensive, Cloud & Infrastructure)
Part-Time, Remote
About idlexperts
idleXperts is idler's quality assurance network — the part-time, remote contributors ("experts") who review idler's reinforcement-learning environments before they're delivered to customers. Experts are the last check between what engineering builds and what a customer receives: they confirm a task is solvable as given, confirm the grading actually tests the right thing, and flag anything that would make the task unfair or ambiguous to an AI model attempting it.
About the Role
We're building a dedicated bench of cybersecurity reviewers to QA our defensive-security and infrastructure-security RL environments: detection-engineering tasks, incident-response and root-cause-analysis tasks, cloud/IAM misconfiguration tasks, and digital-forensics tasks. You'll work through environments we've already built and tell us whether they're fair, correct, and ready to ship.
Part-time, flexible-hours contract work; we'll scope weekly workload directly with you.
What You'll Do
- Review a described incident, misconfiguration, or detection gap and confirm it's realistic, reproducible from the given logs/environment, and solvable by a skilled practitioner using only the provided context.
- For detection/remediation tasks, confirm the grading actually distinguishes a correct fix from an incorrect or incomplete one (e.g., a rule that only coincidentally matches the sample data).
- For forensics tasks, confirm the planted artifact is genuinely recoverable through sound method, not by guessing.
- Flag inconsistent files, missing context, unclear requirements, or anything that would make the task unsolvable or ambiguous.
- Write a short, clear justification for every decision.
What We're Looking For (Minimum Qualifications)
- Hands-on experience in at least one of: SOC/detection engineering, incident response, cloud security (AWS/GCP/Azure IAM and misconfiguration), or digital forensics.
- Comfortable working with logs, cloud consoles/CLIs, and a common detection-rule language (e.g., Sigma, KQL, or a SIEM's native query language), or standard forensic tooling.
- Able to clearly explain, in writing, why a finding is or isn't realistic and correctly graded.
- Reliable and self-directed, comfortable working async with limited oversight.
Nice to Have
- A certification such as GCIH, GCFA, GREM, CISSP, or a cloud-security credential (e.g., AWS Security Specialty).
