Role OverviewHarvey is building a world-class support function, and the pace at which our product evolves means enablement is the infrastructure our customer experience runs on. We're looking for a Manager of CX Knowledge & Enablement to build that infrastructure and lead the team that owns it. The function is new as of this quarter. The foundation exists; most of what it needs to become does not.
Reporting to the CX Operations Manager, you will manage the Enablement team and own the knowledge infrastructure that customer-facing and AI-assisted work runs on, along with the competency and quality programs built on top of it: onboarding, ongoing product and process training, and quality assurance. You'll define what this function is responsible for, what it deliberately isn't, and the roadmap that gets it there.
Your goal is to help CX specialists succeed: build confidence, develop the skills to handle complex customer issues, and consistently meet their performance targets through practical learning and support tailored to their needs.
You'll lead a team that carries the execution, and you'll stay hands-on through a build phase where most of the system doesn't exist yet: designing the architecture, writing the first version of the standards, proving the model before handing it off. This is a role for someone with a point of view on where support enablement is headed in an AI-assisted org, and the bias for execution to get there.
What You'll Do- Own the knowledge infrastructure strategy end to end: the architecture, the consolidation path off our current fragmented surfaces, the governance model, and the platform decisions. This covers the full content surface: enablement and training, product best practices, troubleshooting, and product support.
- Architect that knowledge to be structured, retrievable, and reliable enough to serve both human and AI consumers.
- Own the standards for support competency at Harvey: what "ready" means from onboarding through mastery, how it's measured, and how readiness is maintained against a product that changes weekly.
- Own the quality system as the diagnostic layer on top of the infrastructure. Where knowledge is wrong, missing, or unfindable it should route back into the platform rather than into a training backlog.
- Build the release-to-readiness loop with Product and Product Ops and the mechanism that converts what's shipping into organizational readiness ahead of the ticket volume.
- Design a balanced, audience-appropriate learning strategy. Choose the right mix of instructor-led sessions, self-paced modules, written documentation, short-form video, peer learning, and in-workflow guidance for each audience, type of content, and level of urgency.
- Build and lead the team that maintains the system: hire ahead of need, set a high bar, and scale the team's capability in step with the organization it serves.
- Measure the system by its downstream outcomes: specialist ramp time, attainment of performance targets, speed to competency on new releases, resolution quality, and deflection through self-serve and agent-handled resolution. Combine quantitative metrics with structured specialist feedback, turn the findings into targeted action plans, and report on effectiveness to leadership
- Use AI in the work itself, not just as a consumer of it: generating and maintaining content, detecting staleness and gaps from ticket signal, and automating the parts of enablement that shouldn't need a person. Treat the team's own leverage as something to keep raising.
- Partner with the AI Support team, Engineering, Product, and GTM Enablement to make sure the knowledge layer serves every surface that depends on it, and own the agent-facing content itself.
What You Have- 8+ years across knowledge management, support enablement, education, trust & safety, or a related function, including experience building or leading a team and owning a system rather than a program.
- Demonstrated experience in architecting knowledge to be serviceable at scale. You've built a knowledge system serving a large user and AI agent population, and you can speak to the architecture decisions and what they bought you.
- A point of view on what enablement becomes when agents handle the first line and specialists only see the hard cases: how the competency bar moves, what training is for, and which parts of the traditional playbook stop applying. You're rebuilding the function for where support is going, not porting what worked before.
- Track record of owning a consolidation or platform migration that retired fragmented surfaces and got an organization onto one system.
- Experience launching, managing, and improving knowledge or training platforms, with measurable operational improvement to show for it.
- Data-driven approach to measuring impact: comfortable defining success metrics, instrumenting ramp, quality, and resolution outcomes, and using what they show to decide where to invest.
- Strong instructional design range, including the judgment to choose the right format for each learning need and to adapt content for different audiences, tenure levels, and learning preferences.
- Experience working in fast release cycles and adapting content rapidly as products and processes evolve.
- Deep familiarity with AI-assisted support workflows and what it takes for an agent and a specialist to handle complex technical customer issues with confidence.
- Experience building content for AI systems as well as people, or a clear point of view on what changes when both are consuming the same knowledge base.
- Strong cross-functional instincts, you know how to partner across Product, Engineering, AI, and GTM to get the right things built and adopted without needing to own the technology yourself.
- Excellent communication skills and the presence to establish credibility for your team's work across the organization.
- Bonus: experience supporting a legal, regulated, or otherwise high-stakes customer base.
Compensation$144,000 - $216,000 USD
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