Forward Deployed Engineer, AI
Posted 2 days ago
The role in short
Embed with hardware engineering teams to build custom agentic workflows and skills on the Nominal platform, turning customer needs into reusable product features.
About Nominal
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.
About Hardware Intelligence
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.
The Role:
As an AI Forward Deployed Engineer, you'll embed with hardware teams, build agentic workflows and skills on Nominal, and bring back the patterns that should become product.
💼 What You'll Do:
- Embed with hardware engineering teams at build-partner programs, on site and alongside them.
- Build custom agentic workflows and skills for each partner's programs that do real engineering work: turning requirements and test plans into checks, running analyses, and investigating issues.
- Spot the patterns across customers and bring them back as product: new skills, agent capabilities, and eval tasks.
- Win build partners by proving value: measured before-and-after results on real programs.
- Be the technical voice of the customer inside the Hardware Intelligence team.
🚀 What you'll bring:
- 4+ years as a software engineer, with strong Python and data skills.
- Hands-on experience building agentic workflows: tool use, prompting, skills, and checking whether they actually work.
- Comfort in front of customers: you can run a working session with senior engineers and earn their trust.
- A builder's instinct for what's reusable and what's one-off.
- Willingness to travel to customer sites.
- 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 been a test, flight test, controls, or hardware engineer before moving into software.
- You've been a forward-deployed or solutions engineer at an AI or data company.
- You've turned a one-off customer build into a reusable product feature.
- You hold an active security clearance, or can obtain one.
Benefits/Perks
- 🏥 100% coverage of medical, dental, and vision insurance
- 🏖️ Unlimited PTO and sick leave
- 🍽️ Free lunch, snacks, and coffee
- 🚀 Professional Development Stipend
- 🛠️ In-office hardware lab with a $250 project stipend
- ✈️ Annual company retreat
- 👶 12 weeks parental leave
- 🌱 Fertility and family planning ($15,000 lifetime)
- 💪 Wellness stipend ($100/month)
- 🛠️ $500/year hardware sensor stipend
- 💼 401(k)
- 📈 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.
ITAR Requirements
To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here .
Compensation
The base pay range for this role is $220,000 – $270,000 per year.
Prep for this interview
3 questions likely to come up at Nominal, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “Hands-on experience building agentic workflows: tool use, prompting, skills, and checking whether they actually work.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Cloud, deployment and security
They wrote: “You hold an active security clearance, or can obtain one.”
The customer's security team sends a 200-question review. How do you handle it?
How to answer
Why they ask: Security reviews often block deals. FDEs help get through them.
- Start from your company's standard answers (SOC 2 report, past reviews)
- Answer clearly; don't oversell
- Bring in your security team for anything you're unsure of
- Keep the new answers for the next customer
Tip: Reuse past answers, flag honest gaps, and offer a call for the hard questions.
- 3Coding
They wrote: “4+ years as a software engineer, with strong Python and data skills.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
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