Product Support Engineer

Nominal•New York, NY

3d•Onsite

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 looking for a Product Support Engineer who sits at the intersection of software, product, and customer experience. You're closer to a product engineer than a ticket resolver. You'll spend your time understanding how technical customers use the product, debugging real engineering problems, partnering with the teams building Nominal, and turning what you learn into a better product.

Requirements

Strong Technical Fluency: Comfortable with APIs, relational databases like Postgres, MySQL, or ClickHouse, and observability tools like Datadog. You can write Python, navigate a codebase, and work comfortably in GitHub.
A Builder Mindset: You enjoy figuring things out yourself. You don't need a perfectly documented playbook and are comfortable digging into unfamiliar systems to find the answer.
Customer Empathy: You like working directly with technical users and can translate a customer's problem into a clear technical issue or product opportunity.
Debugging Instincts: You know how to follow a problem through logs, data, APIs, and code to get to the root cause rather than stopping at a workaround.
Product Thinking: You can recognize when a customer issue points to a deeper product problem and advocate for changes that improve the experience for more than one customer.
Cross-Functional Strength: You can work seamlessly with engineering and product, communicate technical context clearly, and keep issues moving without waiting for someone else to take ownership.
Clear Communication: You can explain complex technical problems simply, whether you're talking to an engineer, a customer, or an executive.

Nice To Haves

Hardware Curious: Experience with hardware development, testing, measurement, validation, or other engineering environments is a strong plus.
Familiarity With Technical Data: Experience with tools or formats like LabVIEW, MATLAB, MCAP, or HDF5 is a plus.
Data Skills: Strong SQL and Python skills, including experience with Pandas or similar data analysis tools.

Responsibilities

Own Technical Problems End-to-End: Take ownership of complex customer issues from initial investigation through resolution. You’ll dig into the problem, identify the root cause, and drive it to closure.
Work Directly With Engineers: Partner closely with highly technical customers to understand how they use Nominal, troubleshoot issues, and help them get the most out of the product.
Debug the Real Thing: Dive into logs, APIs, databases, code, and production systems to understand what’s happening. You won’t just be routing tickets, you’ll be solving problems.
Partner With Product & Engineering: Bring customer problems directly to the teams building the product. Help distinguish bugs from product gaps, influence priorities, and push issues toward lasting solutions.
Turn Support Into Product Improvements: Identify recurring patterns across customers and use those insights to improve the product, documentation, tooling, and overall customer experience.
Build the Knowledge Base: Create technical documentation, troubleshooting guides, and internal tools that make the team and our customers more self-sufficient.
Use AI to Work Smarter: Leverage AI to accelerate debugging, investigate issues, automate repetitive workflows, and build tools that make technical support more scalable.
Own High-Impact Moments: When something breaks or a customer is blocked, you’ll be the person who jumps in, figures out what’s happening, and keeps everyone moving.