Software Engineer, AI Platform

Nominal•Los Angeles, CA

About The Position

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 Hardware Intelligence (HINT): 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. ABOUT THE ROLE 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.