About the role

Staff Software Engineer owning AI-powered go-to-market systems at Square. Designing scalable sales automation and integrations that improve seller-facing commerce operations.

Responsibilities

Own the technical strategy, architecture, and long-term evolution of the go-to-market technology stack.
Design, build, and operate production-grade AI agents and automated workflows that reduce manual sales work, improve decision-making, and give time back to sales teams.
Lead technical execution across initiatives by setting direction, breaking down complex problems, reviewing designs, and helping engineers deliver high-quality solutions.
Remain hands-on throughout the software lifecycle, from prototyping and architecture through implementation, testing, deployment, and production operations.
Partner with Sales and Sales Strategy & Operations to understand workflows, identify high-value opportunities, and translate business problems into scalable technical solutions.
Establish patterns, evaluation methods, and guardrails for reliable, observable, secure, and safe AI systems handling sensitive business data.
Define success measures and use data, evaluations, and user feedback to improve accuracy, adoption, and business impact.
Make build-versus-buy decisions and guide integrations across internal platforms, CRM systems, data services, and third-party tools.
Build alignment with senior technical and business partners, communicate complex tradeoffs, and raise engineering standards through mentorship and design reviews.

A minimum of 12 years of related experience with a Bachelor’s degree; or 8 years and a Master’s degree; or a PhD with 5 years experience; or equivalent experience.
Experience setting technical direction and delivering complex, business-critical systems across multiple teams or functional areas.
Experience designing, building, and operating production software that uses large language models, AI agents, tool calling, retrieval, workflow orchestration, or related technologies.
A track record of moving AI capabilities from prototype to production, including evaluation, observability, security, reliability, and human oversight.
An AI-native approach to engineering, using AI tools throughout development while applying sound judgment about when to use models, deterministic software, or human review.
Strong software engineering and system-design skills, with the ability to work hands-on across application, service, integration, and data layers.
Experience leading technical execution through influence, mentorship, architecture, and direct contribution, without relying on formal reporting authority.
The judgment to navigate ambiguous problems, evaluate technical and business tradeoffs, and influence senior engineering and business partners.
Familiarity with sales processes or go-to-market systems—such as lead management, opportunity management, forecasting, account planning, or customer relationship management platforms—is helpful but not required.