Staff ML Engineer - Time Series & Statistical Methods

Nominal; New York (NY); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

100% coverage of medical, dental, and

vision insurance

Unlimited PTO and sick leave

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 high-rate telemetry data. You will define ML strategy for hardware data, spanning classical methods to deep learning, and influence the technical roadmap for ML across the team.

You will build anomaly detection and forecasting systems, package methods as agent tools, and collaborate with evals to validate performance before engineers integrate them into

Qualifications

• 8+ years in applied ML or statistics with production systems on time-series or sensor data.
• Strong grounding in statistics, signal processing, anomaly detection, and forecasting.
• Hands-on deep learning with transformers and sequence models for sensor data.
• Proficient software engineering; production-ready code.
• Ability to set technical direction across a team.

Responsibilities

• Build anomaly detection, change-point detection, and forecasting for high-rate telemetry.
• Package methods as agent tools for real-time feature engineering.
• Develop signal-processing methods that work with noisy real-world data.
• Collaborate with evals to measure readiness for engineers.
• Define ML tech roadmap and balance learned models vs classical methods.

Skills

Applied ML

Time-series

Statistics

Signal processing

Deep learning

Software engineering

Technical leadership

AI coding agents familiarity

Tools

Transformers

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 high-rate telemetry data. You will define ML strategy for hardware data, spanning classical methods to deep learning, and influence the technical roadmap for ML across the team.

You will build anomaly detection and forecasting systems, package methods as agent tools, and collaborate with evals to validate performance before engineers integrate them into