AI Deployment Lead

SuperDial•New York, NY

8d•$165,000 - $195,000•Hybrid

About The Position

We’re hiring an AI Deployment Lead to own one of the most important moments in the customer journey: a customer’s first operational experience with SuperDial. This role owns trial execution from customer and use-case intake through results delivery and handoff into deployment. You’ll work closely with Sales, Customer Success, Product, Engineering, Quality, and Agent Operations to ensure trials are clearly scoped, operationally sound, measured accurately, and translated into insights that help customers and internal teams make confident decisions. This is not a coordination role. We’re looking for a strong operator who can bring structure to ambiguity, diagnose what is driving performance, and build repeatable systems that make trial operations more consistent, measurable, and scalable. This role will report to the Director of Agent Operations and is based in New York, NY with a hybrid in-office model.

Requirements

7+ years of relevant experience in operations, strategy and operations, product operations, implementation, technical solutions, customer operations, consulting, or another execution-heavy environment.
Experience owning complex workflows, customer deployments, internal systems, operational processes, or cross-functional initiatives end-to-end.
Experience operating in a customer-facing or commercially visible environment where quality, speed, communication, and outcomes all matter.
Strong ability to define metrics, diagnose quality or performance issues, and communicate outcomes clearly.
Demonstrated ability to work cross-functionally across Sales, Customer Success, Product, Engineering, Quality, or Operations.
Strong analytical judgment and comfort using spreadsheets, dashboards, structured analysis, or operational data to investigate problems and drive decisions.
Experience using AI tools, automation, dashboards, spreadsheets, or workflow systems to improve operational quality, speed, visibility, or repeatability.

Nice To Haves

Experience in healthcare, revenue cycle management, call center operations, marketplace operations, workflow automation, or other operationally complex environments.
Experience with AI-enabled workflows, automation, QA/QC systems, workflow tooling, routing logic, or data-heavy operations.
Experience in implementation, technical customer success, product operations, pre-sales/post-sales solutioning, or consulting.
Experience in a high-growth startup or scaling environment where processes and systems are still being built.
Experience partnering with offshore teams, BPOs, vendors, or external delivery partners.

Responsibilities

Partner with Sales and Customer Success to understand each customer’s workflow, use case, success criteria, constraints, and decision-making context.
Translate ambiguous customer needs into clear operational requirements, workflows, scripts, inputs, quality expectations, and success metrics.
Identify risks early, including unclear scope, poor input quality, workflow complexity, missing context, AI performance gaps, or operational execution issues.
Track and improve key trial metrics, including batch completion rate, trial success rate, turnaround time, quality outcomes, and readiness for deployment.
Use data and operational judgment to understand what is driving performance.
Distinguish between one-off execution misses and deeper systemic issues across inputs, workflow design, scripting, AI behavior, tooling, quality review, or process.
Surface crisp, commercially relevant insights that inform customer conversations, expansion opportunities, deployment readiness, and internal prioritization.
Partner with Agent Operations, Product, Engineering, Quality, Customer Success, and Sales to resolve issues that impact trial performance.
Drive corrective actions across workflows, scripts, inputs, quality review, tooling, automation behavior, and operating processes.
Escalate thoughtfully and ensure the right owners have the right context to act.
Ensure customer context, trial learnings, scripts, workflow nuances, quality issues, and operational decisions are clearly handed off to the deployment operator or steady-state owner.
Create and improve trial playbooks, intake templates, operating cadences, dashboards, runbooks, escalation paths, and handoff processes.
Use AI tools, automation, and structured workflows to reduce manual effort, improve consistency, and make trial operations more scalable.
Turn recurring pain points into repeatable systems, templates, prompts, workflows, or tooling improvements.
Partner with Product, Engineering, and Agent Operations to understand where AI behavior, workflow logic, tooling, or input quality is affecting trial performance.