Senior Manager, GTM Sales Engineer & AI solutions

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

Retail is Toast’s biggest bet and we are growing rapidly. The opportunity is massive, and the category is ours to define. You’ll have the backing of Toast’s scale, brand, and resources but you’ll be on the team architecting how we operate, scale, and win. The decisions you make in this role will shape how Toast competes in retail for years to come. Toast is prioritizing AI integration across the enterprise to redefine GTM velocity. As part of this strategy, multiple business units are pursuing specialized AI initiatives tailored to market challenges. This model balances corporate-wide coordination with vertical accountability: teams work in lockstep to scale efficiencies and aggregate impact, while remaining responsible for developing, building, and launching solutions optimized for unique operating environments.

The GTM Sales Engineer & AI solutions is the core functional driver of this transformation within the Retail vertical. As an expert in applied AI, you will own the centralized deployment of AI applications to directly improve Productivity Per Rep (PPR). You will drive AI-powered productivity and automation across Retail sales, working with AEs and BDR/SDR teams to accelerate sales and with Onboarding to streamline the path to Customer Go-Live, with the opportunity to grow your impact across other GTM and client-facing teams as the vertical scales. You will be the operational linchpin running the Retail AI GTM Pod, architecting a continuous feedback loop sourcing grassroots ideas from the frontline, filtering them through a business lens, and scaling them into production tools.

This is a technical, builder-focused role. You will observe decentralized AI applications working across the organization and partner to accelerate their impact, making them structurally better, integrating them into core systems, and maintaining them long-term. While you will partner with central corporate operations and marketing to advocate for Retail use cases and scale company-wide efficiencies, you will also possess the autonomous engineering capabilities to construct vertical-specific pipelines when distinct merchant workflows require it. This role sits in Revenue Operations, but will also dotted-line report into engineering.

A Day In The Life (Responsibilities) AI GTM Pod Governance & Solution Sourcing

• Leadership & Facilitation: Stand up the Retail AI GTM Pod to surface, qualify, and scale AI applications across the Retail field. The model could be a working group of AI-forward reps, managers, and leaders organized around defined themes in Sales & Onboarding, contributing domain expertise and frontline signals. The pod functions as an intake mechanism and sounding board, surfacing field experiments, pressure-testing priorities, and keeping your roadmap grounded in what moves the business.
• Idea & Solution Sourcing: Serve as the production engine for ideas surfacing through the pod and field. Evaluate what is worth scaling, determine the right build, buy, or borrow path, and take the strongest ideas from experiment to production tool—launching with Enablement to non-technical end-users and maintaining active ownership through adoption tracking, feedback, and iteration.
• Build vs. Buy Evaluation: Lead technical discovery to determine the most efficient path for problem statements, assessing whether to build a custom agentic solution, stitch existing APIs, or coordinate with vendors to purchase a scalable product.
• Roadmap Prioritization: Translate decisions into a sprint-based roadmap, prioritizing developments based strictly on their potential to drive vertical PPR and eliminate administrative friction.

Applied Engineering & Systems Integration

• Agile Sprint Execution: Maintain and execute against a technical scrum board, operating on defined sprint cycles to continuously ship, test, and iterate on AI-powered features.
• Industrializing Frontline Wins: Take successful, unscaled local experiments (e.g., territory expansion scoring, contact discovery pipelines, multi-intent category parsers) and harden them with error handling, version control, scalable code, and secure coding practices like input validation, secrets management, and mitigation of LLM risks (e.g., prompt injection).
• Systems Inte…