8+ years of experience in building production systems, particularly with LLM or agent systems.
Strong fundamentals in distributed systems and backend reliability.
Informed opinions on agent architecture and adaptability based on data.
Proven track record of setting technical direction and mentoring engineers.
Experience with modern AI coding agents and a willingness to adapt to new tools.
Responsibilities
Design and build the agent harness for orchestration and long investigations.
Own the shared tool layer for both internal and external agents.
Develop methods for agents to assemble context from diverse data sources.
Make informed decisions on model routing, cost, and latency.
Collaborate with evaluation teams to measure quality and with platform teams on context growth.
Set architectural standards for engineering teams and conduct design reviews.
Benefits
100% coverage of medical, dental, and vision insurance.
Unlimited PTO and sick leave.
Free lunch, snacks, and coffee.
Professional Development Stipend.
Annual company retreat.
Full Job Description
The Role:
As a Staff Engineer supporting agents, you'll design how Nominal's agents work: you'll architect the one platform our agents, the Nominal MCP, and our internal agent all run on, and set technical direction for the team that builds on it.
What You'll Do:
Design and build the agent harness: orchestration, planning, tool calling, memory, and background agents that run long investigations on their own.
Own the shared tool layer that our agents and external agents (via MCP) both use, so every capability we build works everywhere.
Build how agents assemble context: retrieval across test data, documents, designs, and simulations, including sensor data far too large to fit in a context window.
Make model choice a decision, not a dependency: routing, cost, latency, and fallbacks across frontier models.
Partner with evals to make quality measurable, and with the platform team on how the context layer grows.
Set the architecture other engineers build on, and raise the bar through design reviews and mentorship.
What you'll bring:
8+ years building production systems, including real experience shipping LLM or agent systems to users.
Strong distributed-systems and backend fundamentals; you've owned systems where reliability mattered.
Opinions about agent architecture (tool design, context management, autonomy, failure handling) and the judgment to change them when data says so.
A track record of setting technical direction across a team and raising the bar for the engineers around you.
You build with modern AI coding agents (Claude Code, Cursor, Codex) every day, and stay curious and open to better ways of working. The tools keep changing, and so do we.
Nice to have:
You've built an agent harness or LLM platform that other engineers or customers build on, at an AI-native company.
You've shipped long-running, autonomous agents to production and know where they break.
You've contributed to MCP, agent frameworks, or open-source LLM infrastructure.
You've built software for physical systems: robots, vehicles, aircraft, or the test stands behind them.
Benefits/Perks
100% coverage of medical, dental, and vision insurance
