Block
Staff Software Engineer, Go-to-Market Systems & AI
$264K–$395K
New York, NY, United States of America +1 · Remote · Staff · Full-time · Posted Aug 31, 2026
- Location
- New York, NY, United States of America
US - NY - NYC - Remote - Experience
- 12 years with a Bachelor’s degree; or 8 years with a Master’s degree; or a PhD with 5 years of experience; or equivalent experience.
- Education
- Bachelor’s degree, Master’s degree, or PhD, with the stated experience alternatives; or equivalent experience.
About the job
Own the architecture and delivery of Block’s go-to-market systems, including AI agents and automated workflows that improve sales productivity and business performance.
What you'll do
- Set technical strategy and long-term architecture for the go-to-market technology stack.
- Build and operate AI-powered production systems and workflows, with evaluation, observability, security, reliability, and safety guardrails.
- Lead cross-functional technical execution, partner with Sales and other teams, and mentor engineers.
What they ask for
- Experience delivering complex, business-critical systems across multiple teams or functional areas.
- Experience building and operating production software using LLMs, AI agents, tool calling, retrieval, workflow orchestration, or related technologies.
- Track record of moving AI capabilities from prototype to production, including evaluation, observability, security, reliability, and human oversight.
- Strong software engineering and system-design skills; hands-on experience across application, service, integration, and data layers.
Nice to have
- Familiarity with sales processes or go-to-market systems, such as lead or opportunity management, forecasting, account planning, or CRM platforms.
Pay
$263,600 — $395,400 USD (base salary)
Tools
large language models, AI agents, tool calling, retrieval, workflow orchestration, customer relationship management systems
AI focus
Build and operate production AI agents and automated sales workflows; establish evaluation methods and guardrails; improve accuracy, adoption, and business impact using data and feedback.
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