Staff Software Engineer, AI Platform

About Sapiom

Sapiom is building payment infrastructure for AI agents. We enable agents to make purchases for people, manage payments between businesses, and buy the compute, inference, and APIs they need to operate.
Through one integration, Sapiom connects agents to funding sources and payment methods. Our intent verification checks that each transaction matches what the person or business authorized. Spending controls define where and how much agents can spend, with a record of every transaction and its outcome. We're building toward a world where people and businesses can delegate payments to agents with confidence.
We're a small team in San Francisco with deep experience in payments and infrastructure. Our founder was Director of Engineering in Payments at Shopify for five years. We've raised $50 million in our first 12 months from Accel, Dragonfly, Coinbase Ventures, Okta Ventures, Menlo Ventures and Anthropic.

As a Staff AI Platform Engineer, you'll help define the technical direction of the platform from the ground up. You'll architect distributed systems that power AI agents in production, establish engineering best practices, and partner closely with leadership to shape both the technology and the company.

This is an opportunity to join early and have an outsized impact on the architecture, culture, and product strategy of an AI infrastructure company.

• Architect the core platform that powers AI agents in production.

• Design distributed systems for agent orchestration, execution, memory, tool calling, workflow coordination, and communication.

• Build reliable infrastructure that enables AI agents to operate safely and efficiently across enterprise environments.

• Lead the technical design of foundational platform capabilities, balancing performance, reliability, extensibility, and developer experience.

• Drive architecture decisions across backend systems, APIs, infrastructure, and AI runtime services.

• Establish engineering standards for scalability, observability, security, and operational excellence.

• Partner with product and engineering leadership to translate emerging AI capabilities into production-ready platform features.

• Mentor engineers through design reviews, technical guidance, and hands-on collaboration.

• Lead complex, cross-functional initiatives from concept through production.

• 8+ years of experience building large-scale backend systems, distributed systems, or developer platforms.

• Experience operating technical leadership at the Staff or Principal Engineer level, or demonstrated equivalent scope and impact.

• Strong programming skills in Python, Go, Typescript, or similar languages.

• Experience building developer platforms, SDKs, or API-first products that prioritize reliability, scalability, and developer experience.

• Experience working with modern AI systems, including LLMs, tool calling, structured outputs, retrieval, or agentic workflows.

• Strong systems thinking with the ability to simplify complex technical problems.

• A track record of driving technical strategy while remaining hands-on.

• Experience with event-driven architectures, workflow engines, or distributed execution systems.

• Experience building production AI agent platforms or orchestration frameworks.

• Familiarity with LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, Temporal, or similar orchestration technologies.

• Experience with vector databases, retrieval systems, and knowledge infrastructure.

• Experience with LLM serving technologies such as vLLM, SGLang, or TensorRT-LLM.

• Contributions to open-source infrastructure or AI projects.

We're building the infrastructure that will power the next generation of AI applications.

Rather than building a single AI product, we're creating the platform developers use to build intelligent, autonomous systems that can coordinate work, integrate with enterprise software, and execute complex workflows reliably.

As one of the earliest senior engineers, you'll have significant influence over our architecture, engineering culture, and product direction. You'll help define the technical foundation of a company solving some of the hardest problems in production AI.