About this role
Summary: Nominal seeks a Staff ML Engineer to build and package time series, statistical, and signal-processing methods for hardware intelligence agents. The role involves developing anomaly detection, forecasting, and deep learning solutions for high-rate telemetry while setting the technical roadmap for ML at the company.
### 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 a Staff Machine Learning Engineer, Time Series & Statistical Methods, you'll give our agents real numerical tools, because LLMs alone can't reason well over millions of sensor samples, and you'll set Nominal's direction on ML for hardware data, from classical methods to deep learning.
### 💼 What you'll do
- Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry.
- Package these methods as agent tools, so our agents can run real feature engineering and statistics instead of guessing.
- Develop signal-processing and statistical methods (frequency analysis, trend fitting, run-to-run comparison) that hold up on noisy, real-world data.
- Partner with evals to measure when a method is good enough to put in front of engineers.
- Set the technical bar and roadmap for ML at Nominal, including when, and whether, to invest in learned models over classical ones.
### 🚀 What you'll bring
- 8+ years in applied ML or statistics, with production systems on time-series or sensor data.
- Deep classical grounding: statistics, signal processing, anomaly detection, and forecasting.
- Hands-on deep learning: transformers, embeddings, and representation learning for sequences and sensor data.
- Strong software engineering; your methods ship as reliable, tested code.
- Judgment about simple-versus-complex: you know when a well-chosen statistical test beats a neural net, and when it doesn't.
- A track record of setting technical direction across a team and raising the bar for the engineers around you.
- 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 built anomaly detection or forecasting that drove real operational decisions, in observability, predictive maintenance, or vehicle telemetry.
- You've worked with telemetry from aircraft, vehicles, energy systems, or robots.
- You've shipped ML methods as tools that other systems or agents call, not only as models.
- You've worked with the latest models beyond text: VLMs, VLAs, multimodal transformers, or foundation models for robotics and physical systems.
### ✨ 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.*