Staff ML Engineer: Time Series & Statistical Methods

Nominal; San Francisco (CA); On-site; USD 190,000 - 260,000; Full time

Benefits offered by this job

100% medical, dental, vision insurance

Unlimited PTO and sick leave

Free lunch, snacks, and coffee

Professional Development Stipend

Annual company retreat

Job summary

Nominal is hiring a Staff Machine Learning Engineer, Time Series & Statistical Methods in San Francisco. You will empower our agents with numerical tools, extending ML for hardware data from classical methods to deep learning.

You will lead the ML direction, build anomaly detection and forecasting for high-rate telemetry, and work with evals to ensure methods meet real-world engineering needs. Collaboration and hands-on coding are essential.

Qualifications

• 8+ years in 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 sequence modeling.
• Strong software engineering; code is reliable and tested.
• Ability to set technical direction across a team and raise the bar for engineers.

Responsibilities

• Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry.
• Package methods as agent tools so agents can run real feature engineering and statistics.
• Develop signal-processing and statistical methods that hold up on noisy real-world data.
• Partner with evals to measure when a method is ready for engineers.
• Set the technical bar and roadmap for ML at Nominal, including decisions on learned models vs. classical methods.

Skills

Applied ML

Statistics

Signal processing

Deep learning

Software engineering

Technical leadership

Tools

Claude Code

Cursor

Codex

Job description

Nominal is hiring a Staff Machine Learning Engineer, Time Series & Statistical Methods in San Francisco. You will empower our agents with numerical tools, extending ML for hardware data from classical methods to deep learning.

You will lead the ML direction, build anomaly detection and forecasting for high-rate telemetry, and work with evals to ensure methods meet real-world engineering needs. Collaboration and hands-on coding are essential.