Staff Machine Learning Engineer, Time Series & Statistical Methods

Nominal; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Unlimited PTO

Free lunch, snacks, and coffee

Professional Development Stipend

Annual company retreat

Job summary

Nominal is seeking a Staff Machine Learning Engineer, Time Series & Statistical Methods to empower our agents with robust numerical tools for hardware data analysis. You will bridge classical methods and deep learning to handle high-rate telemetry and test campaigns.

You will define ML direction, collaborate with evals, and raise the bar for engineering as we scale across aerospace, automotive, and defense programs. Seattle on-site role with strong compensation and benefits.

Qualifications

• 8+ years of applied ML or statistics with production systems on time-series or sensor data.
• Deep grounding in statistics, signal processing, anomaly detection, and forecasting.
• Hands-on deep learning with transformers, embeddings, and representation learning for sequences and sensor data.
• Strong software engineering; methods ship as reliable, tested code.
• Judgment on simple versus complex approaches; know when a statistical test beats a neural net.
• Track record of setting technical direction across a team and raising the bar for engineers.

Responsibilities

• Build anomaly detection, change-point detection, and forecasting for high-rate telemetry.
• Package methods as agent tools for real feature engineering and statistics.
• Develop signal-processing and statistical methods for noisy, real-world data.
• Collaborate with evals to measure when a method is production-ready for engineers.
• Set the ML technical bar and roadmap, including investments in learned models vs. classical approaches.

Skills

Applied ML

Time-series

Statistics

Signal processing

Software engineering

Technical leadership

Tools

Claude Code

Cursor

Codex

Job description

About Nominal

Our mission is to accelerate how the world engineers new hardware. Nominal's connected test and operations platform powers the world's most advanced hardware programs and its most ambitious startups, from spacecraft, racecars, and autonomous vehicles to next-generation defense and energy programs. Our customers include Anduril, Shield AI, Hermeus, Albedo, Shinkei, and Pratt Miller Motorsports, as well as U.S. Navy and U.S. Air Force programs. Now we're expanding across the entire hardware lifecycle, building the foundation, AI-native applications, and agents that accelerate innovators' work and change what's possible to build.

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
• 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.