Director of Customer Success (AI)

Location: Hybrid in the San Francisco Bay Area, California, or New York, NY

Compensation: OTE range of $295K–$350K; equity offered

Company

hirify.global provides a unified interface for AI inference, giving developers and organizations access to hundreds of models through a single API for choosing, routing, evaluating, and scaling model usage.

What you will do

• Define and execute an AI-native Customer Success strategy using intelligent systems, automation, and high-impact customer engagement.
• Own the customer journey from onboarding through production adoption, value realization, retention, and expansion across AI-native startups, enterprises, and strategic customers.
• Drive GRR, NRR, customer health, inference consumption growth, strategic renewals, and expansion opportunities in partnership with Sales.
• Build AI-powered workflows, customer intelligence, lifecycle programs, health frameworks, success plans, adoption milestones, and executive business reviews.
• Lead and develop Customer Success Managers while building trusted relationships with developers, technical leaders, founders, and executives.
• Partner with Sales, Forward Deployed Engineering, Product, and Support; use product telemetry, inference usage, customer feedback, and business signals to improve adoption and influence product strategy.

Requirements

• Experience leading and scaling Customer Success at a hyperscaling AI-native company, model lab, or developer-first infrastructure platform.
• 8+ years of experience in Customer Success, account management, or related post-sales functions, including 4+ years leading teams through significant growth.
• Experience building scalable Customer Success programs, workflows, automations, or AI-powered systems that improve customer outcomes and team effectiveness.
• Understanding of APIs, SDKs, LLMs, inference infrastructure, and the challenges of taking AI applications from experimentation into production.
• Proven ability to drive retention, expansion, NRR, GRR, and consumption growth in usage-based or subscription businesses.
• Ability to engage developers, engineering leaders, founders, and C-level executives and translate technical adoption into measurable business value.
• Experience building repeatable customer engagement models that support significant customer and revenue growth without proportional headcount increases.
• Ability to use product usage, customer health, adoption signals, and revenue data to prioritize engagement and drive outcomes.
• High ownership, comfort with ambiguity, and willingness to balance hands-on customer work with long-term organizational development.

Nice to have

• Experience at a leading LLM model lab, AI inference platform, or developer-first AI infrastructure company.
• Experience managing enterprise and AI-native startup customer segments.
• Experience building AI agents, automated customer lifecycle programs, or intelligent customer engagement workflows.
• Familiarity with model routing, inference economics, latency, reliability, and production AI workloads.
• Experience building Customer Success organizations in consumption-based, product-led growth environments.
• Experience partnering closely with Forward Deployed Engineering or technical implementation teams.

Culture & Benefits

• Builder-oriented role focused on experimenting, shipping, and creating scalable systems rather than only managing existing processes.
• AI agents and automation are treated as extensions of organizational operations.
• Hybrid work arrangement in the San Francisco Bay Area or New York.
• Equity offered in addition to the stated OTE compensation.

Hiring process

• First 30 days: understand customers, product usage, the customer journey, and post-sales operations; establish strategic customer relationships and identify improvement opportunities.
• First 60 days: establish customer health and revenue visibility, refine engagement models, and begin implementing scalable workflows.
• First 90 days: establish the Customer Success operating model, measure progress against adoption and retention goals, and create a roadmap for AI- and automation-enabled scaling.