Founding Support Lead

Decagon•San Francisco, CA

3d•$144,000 - $180,000•Onsite

About The Position

Decagon is seeking a Founding Support Lead to establish and own the end-to-end customer support function for their conversational AI platform. This role involves starting with hands-on issue resolution to gain deep product and customer understanding, then building scalable systems including coverage models, tooling, SLAs, and runbooks. The lead will be responsible for 24/7 incident response, daily issue coverage, and managing escalation paths with forward-deployed teams and engineering. This is a builder role focused on defining and scaling a world-class enterprise support function in the AI era, with the opportunity to grow into a leadership position and influence product development through customer feedback.

Requirements

5+ years in a technical, customer-facing role such as technical support, technical account management, solutions, implementation, or support operations, ideally in B2B SaaS serving enterprise customers.
Genuinely technical and integrations-savvy. You can debug API and authentication issues (rotated credentials, tokens, webhooks), reason about how systems connect (for example ticketing platforms like Zendesk), and tell a Decagon problem apart from an upstream one. A systems-thinking mindset is essential.
A builder's instinct. You want to create the processes, tooling, and team, not just work a queue someone else designed. You are energized by ambiguity and a blank page.
A track record of ownership in high-stakes, fast-moving environments, staying calm and decisive during incidents and escalations with demanding enterprise customers.
Excellent communication and judgment. You can explain a technical issue clearly to a frustrated stakeholder, know when to escalate, and follow through until the issue is fully resolved.
Comfort being hands-on first and building the team second, with the ambition to grow into the leader of the function.

Nice To Haves

Experience standing up or scaling a support or technical support function from an early stage, including tooling selection, staffing, and 24/7 or on-call coverage models.
Working knowledge of customer support processes, systems, and best practices, and familiarity with agentic AI or conversational AI deployments in large organizations.
Hands-on experience with enterprise integrations (APIs, webhooks, ERPs, CRMs, ticketing and payment systems) and the ways they break in production.
Experience in a high-growth startup environment, and the willingness to design a follow-the-sun or BPO-supported model as coverage needs grow.
A Bachelor's degree in a Science, Technology, Engineering, or Math field, or equivalent hands-on technical experience.

Responsibilities

Build the Support function: define the coverage model, tooling, SLAs, runbooks, and processes that a world-class enterprise support team runs on, and own how it scales as Decagon grows.
Stand up 24/7 support and incident escalation: build the always-on support and on-call coverage our customers can depend on at any hour, so every account gets a fast, expert response.
Own resolution end to end: triage, resolve, and follow up on customer issues across the full customer base with speed and care, holding quality high as volume grows.
Go deep technically: personally resolve the hardest agent-configuration and integration issues, and become the person who understands Decagon's failure modes better than anyone.
Turn firsthand knowledge into leverage: capture what you learn into runbooks, macros, and playbooks that make every future hire faster and more consistent.
Hire and grow the team: as volume justifies it, recruit, onboard, and lead the support team, growing from an individual contributor into the leader of the function.
Make support a growth lever: become the trusted first point of contact so our forward-deployed teams can focus on deployment and expansion rather than triage, and partner with them on a clean escalation path.
Feed the front lines back into the product: surface recurring issues and customer pain to Product and Engineering so the platform gets more reliable and more self-serve over time.