Member of Technical Staff

About Sapiom

Sapiom is the end-to-end platform that removes barriers to ship and scale agentic products.

We unify everything an agent needs to act in the world: compute and sandboxes, memory, identity, domains and DNS, spend controls, browser automation, web search and deep research, databases, storage, queues, messaging, image generation, voice, enrichment, verification, and monitoring provisioned together as one thing, not handed over as a framework for builders to assemble themselves. Pricing is just as simple: a plan, a generous free tier, pay for what you use when you use it.

We have assembled a world-class team with deep infrastructure and payments DNA to build the operating system for machines. Backed by a $15.75M investment from Accel, Menlo, and Anthropic, we are moving with relentless focus to allow builders to ship and scale agentic products.

About the Role

This is a high-autonomy engineering role at the intersection of AI, infrastructure, and distributed systems.

You’ll design and build the core systems that power Sapiom’s agent platform — the infrastructure that enables agents to access tools, interact with the web, manage state and memory, execute workflows, and operate reliably in production.

The problems are broad and technically challenging. You might work on orchestration and execution systems one day, platform infrastructure or developer tooling the next, and reliability, observability, or integrations after that. We’re looking for engineers who enjoy operating at this level of ambiguity and can move from architecture → implementation → production without needing a lot of structure around them.

You’ll work closely with the founding team and have a meaningful role in shaping both the technology and product as Sapiom evolves.

AI-native development is also a core part of how we work. We use AI tools throughout the engineering workflow and expect engineers to do the same — not as a replacement for engineering judgment, but as a force multiplier.

What You’ll Do

• Own end-to-end delivery of critical platform and infrastructure systems, from architecture through production

• Design and build reliable systems that allow AI agents to execute complex, multi-step workflows

• Build infrastructure for agent execution, orchestration, state, memory, tools, integrations, and external services

• Make pragmatic architectural decisions in ambiguous and rapidly changing environments

• Work across backend, infrastructure, platform, and developer tooling as the problems demand

• Identify and solve the reliability, scalability, performance, and operational challenges that come with running agents in production

• Partner closely with the founding team to shape product and engineering direction

• Establish patterns and systems that allow the engineering organization to move faster as the platform scales

• Use AI-native development tools extensively to accelerate development, debugging, and problem solving

Requirements

• Strong software engineering fundamentals and exceptional coding ability

• Experience designing and building distributed, highly reliable systems

• High agency and a strong sense of ownership — you naturally take problems from “this should exist” to production

• Systems thinker who enjoys solving ambiguous, technically difficult problems

• Comfortable operating across different layers of the stack

• Strong architectural judgment and ability to make pragmatic tradeoffs

• Bias toward shipping, iterating, and learning rather than over-engineering

• Excited about the challenges of building infrastructure for AI agents

• Comfortable working in a fast-moving startup environment with significant autonomy

• Actively uses AI-native development tools as part of your engineering workflow

Nice to Have

• Experience building infrastructure, platforms, or developer tools

• Experience with distributed systems, orchestration, or workflow engines

• Experience building systems that interact with external APIs, services, or infrastructure

• Familiarity with AI/ML systems, LLMs, inference, or agent architectures

• Experience with databases, queues, messaging, storage, or observability infrastructure

• Experience at a high-growth startup or early-stage company

• Experience in payments or financial infrastructure

• Experience working on highly autonomous engineering teams