Nominal builds software that lets hardware teams test, iterate, and deploy as fast as software teams.
The Role
Build infrastructure and AI systems for Hardware Intelligence: agent infrastructure, retrieval and evaluation pipelines, and tooling to reason over large-scale time series, telemetry, and engineering datasets. Prototype and ship AI-powered workflows for anomaly investigation and root cause analysis, work directly with customers, and establish engineering patterns as the platform scales.
Summary Generated by Built In
About Nominal
🚀 About The Role
✅ What You'll Do
⚡️ Skills That Accelerate Us
👀 Nice to Have
✨ Benefits/Perks
Nominal is building the connected test and operations platform powering the world's most advanced hardware systems, from spacecraft and autonomous vehicles to next-generation defense programs. Our platform gives hardware engineering teams a single place to ingest data, analyze performance, automate test execution, and collaborate across every phase of development, so they can move faster without sacrificing safety or precision. We're a fast-moving team that owns problems end-to-end, works across disciplines, and thrives at the intersection of hardware and software.
We serve top-tier commercial and defense customers, from autonomy leaders like Anduril and Shield AI to next-generation aerospace teams like Hermeus and REGENT, and performance engineering teams like Pratt Miller Motorsports, alongside mission partners within the U.S. Navy and U.S. Air Force on programs where failure isn’t an option. We’re backed by Sequoia, General Catalyst, Founders Fund, Lux Capital, and Lightspeed. Our team draws from SpaceX, Palantir, Anduril, Applied Intuition, 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 building AI systems that reason over real-world engineering data to help engineers understand failures, investigate root causes, and accelerate mission-critical work. This is an opportunity to help define an entirely new category of software at the intersection of AI, distributed systems, and physical engineering.
We're looking for an experienced software engineer to join our Hardware Intelligence team. This is a highly ambiguous, zero-to-one engineering role. You'll build the infrastructure, data systems, and AI capabilities that power the next generation of intelligent engineering tools. Rather than implementing predefined product requirements, you'll work alongside customers and product leaders to discover what should exist, and then build it.
Success in this role comes from being comfortable making technical bets, rapidly prototyping new ideas, and turning uncertainty into working software.
- Build the systems that power Hardware Intelligence, including agent infrastructure, evaluation pipelines, retrieval systems, and tooling for reasoning over engineering data.
- Design and ship AI-powered workflows that help engineers investigate anomalies, perform root cause analysis, and understand complex hardware behavior.
- Work across large-scale time series, telemetry, logs, and other engineering datasets to build robust, production-ready systems.
- Prototype new approaches quickly, evaluate them with customers, and iterate based on real-world feedback.
- Partner closely with engineers and product leaders to determine where the team should invest next.
- Help establish engineering patterns, infrastructure, and best practices as Hardware Intelligence scales.
- Engage directly with customers to understand how complex engineering problems are solved today and translate those workflows into product capabilities.
We're less interested in checking every technical box than finding someone who thrives in early-stage environments and enjoys solving difficult, undefined problems.
You might be a great fit if you have:
- 5+ years of experience building AI or machine learning products that shipped to production.
- Experience working with large datasets, time series data, observability platforms, data infrastructure, or machine learning systems.
- A background in ML, data science, applied AI, or adjacent fields that gives you strong intuition for working with data-intensive systems.
- Strong software engineering fundamentals, with experience designing scalable backend or distributed systems.
- Experience taking products from zero-to-one, whether at a startup or inside an entrepreneurial team within a larger company.
- Strong product instincts and curiosity about customer problems.
- Experience working with telemetry, sensors, robotics, aerospace, industrial systems, autonomous systems, or other hardware domains.
- Experience building agentic systems, LLM applications, retrieval pipelines, or evaluation infrastructure.
- Familiarity with time series databases, observability tooling, or data platforms.
- Experience supporting engineers or scientists working with large-scale operational data.
- Prior startup founding experience or experience on an incubation/new products team.
- 🏥 100% coverage of medical, dental, and vision insurance
- 🏖️ Unlimited PTO and sick leave
- 🍽️ Free lunch, snacks, and coffee
- 🚀 Professional Development Stipend
- ✈️ Annual company retreat
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.
Skills Required
- U.S. person or eligible to obtain Department of State authorizations (ITAR requirement)
- Experience building AI or machine learning products shipped to production
- Experience working with large datasets, time series data, telemetry, observability platforms, or data infrastructure
- Background in ML, data science, applied AI, or adjacent field
- Strong software engineering fundamentals with experience designing scalable backend or distributed systems
- Experience taking products from zero-to-one (startup or internal incubation)
- Experience prototyping quickly, evaluating with customers, and iterating on real-world feedback
- Experience with telemetry, sensors, robotics, aerospace, industrial systems, or autonomous systems
- Experience building agentic systems, LLM applications, retrieval pipelines, or evaluation infrastructure
- Familiarity with time series databases, observability tooling, or data platforms
- Prior startup founding experience or experience on an incubation/new products team
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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