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Nominal builds software that lets hardware teams test, iterate, and deploy as fast as software teams.
The Role
Design and build the platform powering Nominal’s AI agents, including orchestration, planning, tool calling, memory, retrieval, model routing, and autonomous background agents. Own the shared MCP tool layer, establish agent architecture and technical direction, improve evaluation and reliability, and mentor engineers. The role requires shipping production LLM or agent systems, strong distributed-systems and backend expertise, and working in-office five days per week.
Summary Generated by Built In
About Nominal
About Hardware Intelligence
The role
💼 What you'll do
🚀 What you'll bring
⚡️ Nice to have
Benefits/Perks
Compensation
Engineers today are building some of the most ambitious machines humanity has ever seen. They should have AI that’s purpose-built for them.
Nominal is building the Hardware Intelligence Platform that gives engineers increased visibility into complex physical systems, helps them make better decisions, and ultimately gets their products into the real world faster. It brings together the systems, data, intelligence, and applications hardware engineering requires to meet the AI moment. Hardware engineering teams use Nominal to simulate, test, manufacture, and operate the physical systems that keep the world moving, from spacecraft and autonomous vehicles to next-generation energy systems and robotics.
Our customers span aerospace, defense, manufacturing, transportation, energy, and robotics. They include Anduril, Shield AI, Wisk Aero, Pratt Miller Motorsports, Antares, and Shinkei Systems, amongst others, alongside programs within the U.S. Air Force and Navy. Our team draws from SpaceX, Palantir, Anduril, Applied Intuition, Tesla, and other leading companies, united by a common mission: giving hardware engineers the tools to build the future with speed, safety, and confidence. We're a fast-moving team that owns problems end-to-end, works across disciplines, and thrives at the intersection of hardware and software.
Some of the world’s leading technology investors, including Sequoia, Lightspeed, Founders Fund, General Catalyst, Lux Capital, and Insight Partners, are backing Nominal’s ambition to change how machines are built and operated. We’re looking for engineers who want to take on hard problems and help make a new generation of world-changing hardware possible.
We're the team behind Nominal's agents, AI-native applications, and MCP, and its forward-leaning AI bets. Our mission is to unlock the bottlenecks of the hardware lifecycle with AI. Our agents reason over physical reality, from high-rate telemetry and test campaigns to designs and simulations, where real test results are the ground truth their work is checked against. We believe opinionated AI, built for the real work of hardware programs, will change how the world engineers.
We're collaborative, iterative, and high-agency, and we're human-centered and customer-focused. We build with the newest AI tools every day, and because those tools keep changing, so do we: we stay curious and keep looking for the better way. Our team spans data science and ML, distributed systems, search, and knowledge systems, and we obsess over how agents can be genuinely useful to the engineers who rely on them.
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.
- 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.
- 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.
- 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.
- 🏖️ Unlimited PTO and sick leave
- 🍽️ Free lunch, snacks, and coffee
- 🚀 Professional Development Stipend
- ✈️ Annual company retreat
- 🛠️ Hardware sensor stipend
- 📈 Employee-friendly equity terms
- 🏢 In-office 5 days per week
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
The base pay range for this role is £180,000 – £220,000 per year.
Skills Required
- 8+ years building production systems
- Production experience shipping LLM or agent systems to users
- Strong distributed-systems and backend fundamentals
- Experience owning reliable production systems
- Knowledge of agent architecture, tool design, context management, autonomy, and failure handling
- Track record of setting technical direction across a team
- Experience raising engineering standards through mentorship and technical leadership
- Daily use of modern AI coding agents such as Claude Code, Cursor, or Codex
- Experience building an agent harness or LLM platform for engineers or customers
- Experience shipping long-running autonomous agents to production
- Contributions to MCP, agent frameworks, or open-source LLM infrastructure
- Experience building software for physical systems such as robots, vehicles, aircraft, or test stands
Nominal Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nominal and has not been reviewed or approved by Nominal.
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Healthcare Strength — 100% employer-paid medical, dental, and vision coverage is repeatedly described as covering both employees and dependents, indicating unusually strong healthcare provisioning. Additional mentions of “Platinum” coverage reinforce the sense of a high-tier plan offering.
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Leave & Time Off Breadth — Unlimited PTO and paid holidays/sick time are presented as part of the core package, suggesting broad time-off benefits on paper. Parental leave is also framed as fully paid in several places, reinforcing overall leave breadth.
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Wellbeing & Lifestyle Benefits — Wellness stipends, daily gourmet lunch (and sometimes dinner), and periodic retreats are consistently highlighted as meaningful lifestyle perks. Learning/development and other small stipends further round out a perks-heavy total rewards posture.
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The Company
What We Do
Nominal builds the essential software stack that enables hardware teams to test and iterate as rapidly as software teams. Nominal empowers engineers to continuously monitor, validate, and deploy innovations, transforming how mission-critical hardware is built and operated.
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