AI Integration for Greenhouse Job Description Generation

AI integration for Greenhouse job description generation connects at the Job Requisition and Job Post object level, typically triggered when a hiring manager or recruiter initiates a new role. The integration can be invoked via a custom action in the Greenhouse UI, a scheduled workflow, or an API call from your HRIS. Core inputs include the Job ID, Department, Location, and any existing notes or templates. The AI agent then uses this metadata, combined with your company's role libraries and compliance guidelines, to generate a structured draft, which is returned to Greenhouse via the PATCH /v1/jobs/{id} endpoint to populate the Job Post fields like content, internal_content, and custom questions.

The high-value workflow is not just about drafting text, but about operationalizing consistency and compliance. An effective integration will:

• Pull from approved role-based templates to ensure brand and structural alignment.
• Run inclusive language checks against bias dictionaries, suggesting alternatives for flagged terms.
• Embed compliance boilerplate (EEO, pay transparency) based on the job's location data.
• Propose interview plan and scorecard frameworks aligned to the role's competencies. This turns a 45-60 minute manual drafting and review process into a 5-minute review-and-edit cycle, allowing recruiters to focus on strategic intake conversations rather than document assembly.

Rollout should start with a pilot for a specific department (e.g., Engineering) to refine prompts and templates. Governance is critical: all AI-generated content should be logged with version history and require a human approval step before the job post is published. The integration should also support a human-in-the-loop review mode, where the AI suggests multiple options for sections like 'Responsibilities' for the hiring manager to select or edit. For a deeper dive on orchestrating these approval workflows, see our guide on AI Integration for Greenhouse Hiring Workflow Automation.

The integration is triggered when a new Job Requisition is approved in Greenhouse or when a user initiates a draft from a template. The system extracts key requisition data—such as job title, department, location, and required skills—via the Greenhouse API (GET /v1/jobs and GET /v1/job_posts). This structured data, combined with any existing role-specific templates stored in your knowledge base, forms the prompt context for the LLM. The AI generates a complete draft, including sections for responsibilities, qualifications, and company culture, while applying configured rules for inclusive language and compliance formatting.

Generated drafts are written back to Greenhouse as a new Job Post draft via the POST /v1/job_posts endpoint. For governance, the system can be configured to either: 1) auto-submit the draft to Greenhouse's native approval workflow, or 2) place it in a holding queue for human review. A common pattern uses a lightweight orchestration layer (like an AI Agent Builder Platform) to manage this flow, handling retries, logging each generation event for audit, and optionally enriching the draft with EEOC statements or benefits boilerplate pulled from a separate content repository. The result is a compliant, polished draft in the recruiter's queue in minutes, not hours.

Rollout typically starts with a pilot for non-critical roles, using a human-in-the-loop review step to build trust and refine prompts. Key considerations include configuring RBAC in the orchestration layer to control which Greenhouse users or departments can trigger generation, and establishing a feedback loop where recruiter edits to AI drafts are anonymized and used for periodic model fine-tuning. This ensures the integration remains a scalable copilot, not a black-box automation, adapting to your organization's evolving tone and requirements.

Implementation begins by connecting to Greenhouse's REST API using secure, scoped API keys with permissions limited to job_post:write and job:read. All AI processing occurs in a secure Inference Systems environment; candidate PII and sensitive job data are never sent to third-party LLM APIs. Generated drafts are stored as Job Post drafts within Greenhouse, maintaining a full audit trail of who created and approved each version. For compliance-heavy industries, the system can be configured to log all generation prompts, model versions, and user actions for review.

A phased rollout is critical for user adoption and risk management. Start with a pilot group of recruiters or talent partners generating drafts for non-critical roles. Use Greenhouse's custom fields or tags to flag AI-assisted drafts for human review. The initial workflow is simple: a user clicks a "Generate Draft" button in a Greenhouse job, triggering an API call. The system returns a complete draft populated with role-specific competencies, inclusive language, and compliance boilerplate, which the user can then edit and publish. This builds trust before automating bulk or scheduled generation.

For scale, the integration can be extended to trigger automatically when a new Job is created in Greenhouse or when a Job Post enters a specific stage. Governance is maintained through role-based access controls (RBAC)—ensuring only authorized users can generate or auto-publish drafts—and through configurable approval workflows that require a hiring manager's sign-off. The system is designed to be an assistive copilot, not a replacement for human judgment, ensuring final accountability remains with the recruiting team.