Staff Machine Learning Engineer

Who you are

• 8+ years in applied ML or statistics, with production systems on time-series or sensor data
• Deep classical grounding: statistics, signal processing, anomaly detection, and forecasting
• Hands-on deep learning: transformers, embeddings, and representation learning for sequences and sensor data
• Strong software engineering; your methods ship as reliable, tested code
• Judgment about simple-versus-complex: you know when a well-chosen statistical test beats a neural net, and when it doesn't
• A track record of setting technical direction across a team and raising the bar for the engineers around you
• You build with modern AI coding agents (Claude Code, Cursor, Codex) every day, and stay curious and open to better ways of working. The tools keep changing, and so do we
• You've built anomaly detection or forecasting that drove real operational decisions, in observability, predictive maintenance, or vehicle telemetry
• You've worked with telemetry from aircraft, vehicles, energy systems, or robots
• You've shipped ML methods as tools that other systems or agents call, not only as models
• You've worked with the latest models beyond text: VLMs, VLAs, multimodal transformers, or foundation models for robotics and physical systems

What the job involves

• As a Staff Machine Learning Engineer, Time Series & Statistical Methods, you'll give our agents real numerical tools, because LLMs alone can't reason well over millions of sensor samples, and you'll set Nominal's direction on ML for hardware data, from classical methods to deep learning
• Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry
• Package these methods as agent tools, so our agents can run real feature engineering and statistics instead of guessing
• Develop signal-processing and statistical methods (frequency analysis, trend fitting, run-to-run comparison) that hold up on noisy, real-world data
• Partner with evals to measure when a method is good enough to put in front of engineers
• Set the technical bar and roadmap for ML at Nominal, including when, and whether, to invest in learned models over classical ones

Benefits

• 100% covered health, dental, and vision insurance (for you and family)
• $500/year hardware sensor stipend
• 12-weeks parental leave
• In-office 5 days per week
• 401K
• Employee-friendly equity terms
• Unlimited paid vacation
• Wellness Stipend ($100/mo)
• Fertility and family planning ($15,000 lifetime)
• Learning & development stipend
• Gourmet lunch provided daily