Staff ML Engineer - Time Series & Anomaly Forecasting

Nominal; Austin (TX); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

Medical, dental, vision coverage

Unlimited PTO

Free lunch, snacks, and coffee

Professional Development Stipend

Annual company retreat

Job summary

Nominal seeks a Staff Machine Learning Engineer, Time Series & Statistical Methods, to empower our hardware data agents with robust numerical tools. You will influence ML direction for high-rate telemetry and sensor data, from classical methods to deep learning, in a fast-growing hardware AI platform.

You will build anomaly detection, forecasting, and signal-processing methods, packaging them as reusable tools for engineers and agents, and work with evals to ensure production readiness.

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: transformers, embeddings, and representation learning for sequences.
• Strong software engineering; code that ships and is tested.
• Ability to set technical direction across a team and raise the bar for engineers.
• Experience with AI tooling and agents (e.g., Claude Code, Cursor, Codex).

Responsibilities

• Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry.
• Package methods as agent tools for real feature engineering and statistics.
• Develop signal-processing and statistical methods.
• Partner with evals to measure when a method is production-ready.
• Set the technical bar and roadmap for ML at Nominal, including investment decisions on models vs classical methods.

Skills

Applied ML

Time-series data

Deep learning

Signal processing

Software engineering

Model deployment

Evaluation/benchmarks

Tools

Claude Code

Cursor

Codex

Job description

Nominal seeks a Staff Machine Learning Engineer, Time Series & Statistical Methods, to empower our hardware data agents with robust numerical tools. You will influence ML direction for high-rate telemetry and sensor data, from classical methods to deep learning, in a fast-growing hardware AI platform.

You will build anomaly detection, forecasting, and signal-processing methods, packaging them as reusable tools for engineers and agents, and work with evals to ensure production readiness.