Content Engineer

At Furl, we're building the first agentic remediation platform for IT security — technology that closes the gap between identifying risk and actually fixing it. Today, most organizations surface more endpoint security and hardening issues than they can handle. We're changing that by enabling safe, automated remediation across macOS, Windows, and Linux. We ingest security and operational signals, classify them into concrete remediation targets, and use an endpoint agent to collect real-time telemetry and execute fixes. Agentic workflows generate and validate fix plans, which are deployed safely at scale through policy-driven autonomy.

Content is the product. Every remediation Furl executes rests on a working, tested Strategy — and our library grows from Furl-authored, customer-created, and future community-contributed content. Machinery proves it's safe; a human decides it's right. That human is you.

We're a real startup, we collaborate, build trust, and move fast together. It's a fun environment, but we won't sugarcoat it — the pace is demanding and sometimes uncomfortable. We value ownership over hierarchy and believe great work comes from people who are trusted to think, experiment, and take responsibility. You won't just work inside the pipeline; you're accountable for automating it — every manual step you find in authoring, validation, and testing is yours to eliminate.

What You'll Do

As a Content Engineer, you'll own the quality and growth of Furl's Strategy library — authoring new remediation content, managing the intake funnel across every content source, and serving as the human in the loop for our automated content pipeline.

• Author new remediation Strategies in the Furl app — fixes for vulnerabilities, misconfigurations, hygiene gaps, and EOL software across macOS, Windows, and Linux — each with declared verification and revert steps.
• Manage the top of the content funnel: triage and prioritize demand signals (coverage gaps, new CVEs, install-base analytics) and incoming content from Furl engineers, AI-drafted pipelines, customers, and — in the future — community contributions, holding every source to the same quality bar.
• Operate as QA/QC for all new Strategy content: review the output of the automated authoring, validation, and sandbox acceptance-testing pipeline, and accept, modify, or discard content with documented reasoning that feeds the pipeline's learning loop.
• Validate that Strategies do what they claim — the fix applies, the effect is verified, and the revert works — and block anything that doesn't, before it ever reaches a customer endpoint.
• Curate the library: identify duplicate and near-duplicate Strategies, distinguish redundancy from legitimate specialization, and maintain versioning and provenance on every release.
• Build and drive automation across the content lifecycle: AI-assisted authoring from demand signals, automated validation and dedup checks, and sandboxed acceptance testing (apply, verify, revert) — so the library grows faster than manual authoring ever could.
• Work with AI and platform engineers to tune confidence thresholds, sharpen validation checks, and improve the pipeline based on what your reviews catch.

Your Experience

• Background in detection engineering, security content engineering, vulnerability management, or patch management — you've authored, tested, and tuned security content (remediation scripts, detection rules, patch packages, or hardening baselines) for a living.
• Intimate knowledge of system administration across platforms we support (Windows, macOS, Linux).
• Strong scripting ability across platforms — PowerShell and Bash/zsh; Python a plus — and deep familiarity with how macOS and Windows systems are configured, patched, and broken.
• Hands-on QA/QC instincts: experience validating content in lab or sandboxed environments, reducing false positives/failed fixes, and documenting your reasoning.
• Working knowledge of the vulnerability ecosystem — CVEs, scanner findings (Qualys, Tenable, Rapid7), CIS benchmarks, and what remediation actually looks like on a real endpoint.
• A track record of automating yourself out of repetitive work — CI/CD for content, test harnesses, LLM-assisted authoring workflows, or similar pipeline automation.
• Comfort reviewing AI-generated content critically — you trust evidence, not authorship.
• Experience with versioned content libraries, structured review workflows, or contribution/community content models preferred.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor an employment visa.