Staff ML 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

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.