Staff ML Engineer, Time Series & Statistics

Nominal; Los Angeles (CA); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

Unlimited PTO

Free lunch, snacks, and coffee

Professional Development Stipend

Annual company retreat

Job summary

Nominal is building AI-driven hardware intelligence tools to empower engineers across spacecraft, vehicles, and defense programs. You will lead ML for time-series data, developing anomaly detection, forecasting, and signal-processing methods that integrate into agent tools used by frontline engineers.

You will establish the ML roadmap, evaluate when to deploy learned models versus classical methods, and collaborate with cross-functional teams to raise the bar for reliability and performance.

Qualifications

• 8+ years in applied ML or statistics with production-time-series or sensor data.
• Strong grounding in statistics, signal processing, anomaly detection, and forecasting.
• Hands-on deep learning with transformers and sequence representations.
• Proven ability to ship reliable, tested code.
• Experience setting technical direction across teams.
• Experience using AI agents/tools in production is a plus.

Responsibilities

• Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry.
• Package these methods as agent tools for real feature engineering and statistics.
• Develop signal-processing and statistical methods for noisy real-world data.
• Partner with evals to measure when a method is good enough for engineers.
• Set the technical bar and roadmap for ML at Nominal.

Skills

8+ years ML

Classical statistics

Signal processing

Deep learning

Software engineering

Technical leadership

AI agents tooling

Job description

Nominal is building AI-driven hardware intelligence tools to empower engineers across spacecraft, vehicles, and defense programs. You will lead ML for time-series data, developing anomaly detection, forecasting, and signal-processing methods that integrate into agent tools used by frontline engineers.

You will establish the ML roadmap, evaluate when to deploy learned models versus classical methods, and collaborate with cross-functional teams to raise the bar for reliability and performance.