---
title: 'Research Engineer, Hardware Intelligence at Nominal'
canonical: 'https://feeny.ai/job/research-engineer-hardware-intelligence-nominal-new-york-5hqs9dwzjtp2'
type: 'job'
last_seen: '2026-11-11'
---

# Research Engineer, Hardware Intelligence at Nominal

- **Company:** Nominal
- **Location:** New York, NY
- **Compensation:** $230k–$270k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-11-09
- **Last confirmed live:** 2026-11-11
- **Apply:** https://jobs.gem.com/nominal/am9icG9zdDqvOHi7PrPN71mtUPaXxMai

## Job description

## 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 going after the biggest opportunities to accelerate how the world engineers: agents that are expert at engineering physical systems, not just competent at calling tools. Nobody has a recipe for that yet. Hardware gives us an edge most domains lack: real test results to check agent work against. You'll set the research agenda, decide where to turn that ground truth into better context, better tools, or models of our own, run the first experiments yourself, and grow the researchers who run the next ones.

💼 What you'll do

- Find where our agents fail on real hardware work, from customer traces and from tasks you design with hardware experts.
- Turn those failures into evals, environments, and training data grounded in real test results.
- Build graders and verifiers that check agent work against measured data, and find where they break.
- Fix what you find: change a prompt, tool, or context, fine-tune, run RL, or distill an open-weight model.
- Prototype across our research bets: hardware context for agents, computer use in engineering tools, linking simulation to physical tests, coordinating collections of agents, and intelligence at the edge.
- Build the research infrastructure the loop runs on: eval harnesses, dataset and trace pipelines, and training and experiment tooling that make the next experiment faster to run and easy to reproduce.
- Separate real gains from noise, ship what wins into the agents customers use, and write up what you learn.

🚀 What you'll bring

- 3+ years in ML engineering or research. Less is fine if your recent work was doing exactly this at a frontier lab or an AI-native company.
- Hands-on experience improving an LLM or agent system through evals, data, post-training, or harness changes, and proving it worked.
- Strong engineering: you write the pipeline, the training script, and the grader yourself.
- Experimental judgment: you design experiments that don't fool you, and you know when a difference is real.
- Comfort picking the next experiment when nobody can tell you what will work.
- 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 done post-training (SFT, RL from feedback or verifiable rewards, distillation) on real tasks.
- You've built RL environments, synthetic-data pipelines, or benchmarks that other people use.
- You've worked on computer-use agents, multi-agent systems, simulation, or edge deployment.
- You've worked with time-series, sensor, or scientific data, or with models built over it.
- You've been an engineer in hardware, test, controls, or robotics, or worked closely with those teams.

✨ 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](https://www.pmddtc.state.gov/?id=ddtc_kb_article_page&sys_id=24d528fddbfc930044f9ff621f961987).

## About Nominal

## Company Overview
- **One-liner**: Nominal provides a unified industrial data stack that enables hardware engineering teams to test, analyze, and operate mission-critical systems with the speed of software development.
- **Entity Type**: Private (Series B, 2026)
- **Headquarters**: Los Angeles, California, United States
- **Founded**: 2022
- **Founders**: Cameron McCord (Co-founder & CEO)

## Core Business
- Primary industry: Aerospace & Defense, Hardware Testing, Industrial Data Infrastructure
- Target customers: B2B – Engineering teams at startups, enterprises, and government agencies building complex hardware (e.g., autonomous vehicles, space systems, hypersonics)
- Mission: "Equip engineers to test and operate critical technology" – delivering the tools and infrastructure to deliver mission-critical capabilities at scale in the shortest time possible.

## Products & Services
- **[Nominal Core](https://nominal.io)**: A cloud-based collaborative workspace that ingests, structures, and analyzes telemetry, logs, video, and simulation results. Provides real-time mission control, trend analysis, and automated reporting to accelerate development, production, and operations. (SaaS)
- **[Nominal Connect](https://nominal.io)**: An edge compute platform for hardware test. Deploys Python-based test apps on-premises, turns scripts into operator-ready interfaces, and runs tests deterministically at the edge (lab, remote range, air-gapped). Automatically syncs results to Core for deeper analysis. (On-premise/Edge software)

## Market Standing
- **Valuation**: Not disclosed
- **Key Metric**: Total funding of **$182.5M** across 5 rounds (Seed, Series A, Series B, and a non-equity assistance)
- **Notable Investors**: Founders Fund (led Series B), General Catalyst (led Series A), Lux Capital (led Seed), and Sequoia Capital (partnered 2025)
- **Growth Signals**:
  - Headcount grew **+202.5% YoY** to 196 employees (as of 2026)
  - 17,984 LinkedIn followers (+209.7% yearly)
  - Customer Anduril reduced test review time from 5+ hours to near real-time across 300+ engineers
  - Active job postings: 67 (up +346.7% yearly)
  - Offices in Los Angeles (HQ), New York, Austin, Pflugerville (TX)

## Competitive Advantages
- **Unified platform** that replaces fragmented test tools – one data model for telemetry, logs, video, simulations, and documents
- **Built by engineers from Palantir, SpaceX, Anduril, and Lockheed Martin** – deep domain expertise in mission-critical hardware
- **Edge-to-cloud architecture** that works in air-gapped, offline, and secure environments
- **Enterprise-grade security and scale** with support for startups, large enterprises, and government deployments
- **Open API, SDKs, and integration ecosystem** allowing teams to modernize legacy systems without disruption

## Strategic Focus
- Continue scaling the platform to serve larger defense and aerospace programs (e.g., integrated programs, fleet management)
- Expand into manufacturing test lines and production environments (scaling test code from prototype to manufacturing)
- Deepen government partnerships and secure FedRAMP or similar certifications
- Grow headcount aggressively, especially in engineering, mission operations, and sales roles

## Why Work Here
- **Culture**: "Engineers building for Engineers" – a tight-knit team of operators and builders from Palantir, SpaceX, Anduril, and more. High ownership, real impact on hardware that flies, launches, or defends.
- **Work Policy**: In-office **5 days per week** (LA, NY, Austin, Pflugerville). Gourmet lunch provided daily.
- **Perks & Benefits**:
  - 100% covered health, dental, and vision insurance (for you and family)
  - Unlimited paid vacation
  - 12-weeks parental leave
  - Fertility and family planning ($15,000 lifetime)
  - $500/year hardware sensor stipend
  - Wellness stipend ($100/month)
  - 401K with employer match
  - Learning & development stipend
  - Employee-friendly equity terms
- **Growth Trajectory**: Over 200% yearly headcount growth, recent Series B funding, and a clear mission to accelerate the "hardware renaissance" – strong signal for career growth and impact.

## Sources
1. [Nominal Careers Page](https://nominal.io/careers)
2. [Nominal About Us](https://nominal.io/about-us)
3. [Nominal Product Page](https://nominal.io/?j=35813&sfmc_sub=16110646&l=1582_HTML&u=1011363&mid=546014653&jb=234&sfmc_id=16110646)
4. [LinkedIn Company Profile](https://linkedin.com/company/nominal-inc)
5. [Sequoia Capital Portfolio](https://sequoiacap.com/companies/nominal/)

## Other roles at Nominal

- [Social Media Strategist](https://feeny.ai/job/social-media-strategist-nominal-austin-gdf3sanp4va8) — Austin, TX
- [Product Marketing Lead](https://feeny.ai/job/product-marketing-lead-nominal-austin-m6kcctex2n3g) — Austin, TX
- [Revenue Marketing Lead](https://feeny.ai/job/revenue-marketing-lead-nominal-los-angeles-1j8n5ze9kw9q) — Los Angeles, CA
- [Staff Research Engineer, Hardware Intelligence](https://feeny.ai/job/staff-research-engineer-hardware-intelligence-nominal-new-york-dwtp7bvr52em) — New York, NY
- [Software Engineer - Backend - UK](https://feeny.ai/job/software-engineer-backend-uk-nominal-london-1s1c1971dvec) — London, United Kingdom
- [Staff Software Engineer, Agents - UK](https://feeny.ai/job/staff-software-engineer-agents-uk-nominal-london-nf6nqnw0dzw1) — London, United Kingdom
- [Staff Research Engineer, Agent Evals & Post-training - UK](https://feeny.ai/job/staff-research-engineer-agent-evals-post-training-uk-nominal-london-12dab0d5sj69) — London, United Kingdom
- [Graduate Software Engineer/Mission Dev Engineer - London](https://feeny.ai/job/graduate-software-engineer-mission-dev-engineer-london-nominal-london-55cckwj222wd) — London, United Kingdom
- [Staff Product Engineer, AI - UK](https://feeny.ai/job/staff-product-engineer-ai-uk-nominal-london-62y7ememxc5k) — London, United Kingdom
- [Staff Machine Learning Engineer, Time Series & Statistical Methods - UK](https://feeny.ai/job/staff-machine-learning-engineer-time-series-statistical-methods-uk-nominal-nk23teb9vgd4) — London, United Kingdom