# Staff Machine Learning Engineer, Time Series & Statistical Methods at Nominal

- Company: Nominal
- What the company does: Nominal powers mission-critical engineering with advanced data infrastructure, real-time observability, and analysis tools — all in one collaborative platform. Backed by General Catalyst, Lightspeed and Sequoia.
- Company website: https://www.nominal.io/
- Type: Startups (AI role)
- Level: Senior
- Location: New York, United States
- Work setup: On-site
- Posted: 2026-10-05
- Apply by: 2026-11-19
- Apply: https://jobs.gem.com/nominal/am9icG9zdDrUGnLp2Zw0DJdAX-tktqaa?utm_source=1752vc&utm_medium=careers
- Page: https://www.1752.vc/careers/jobs/nominal-staff-machine-learning-engineer-time-series-and-statistical-methods/

## About the role

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. Found on 1752vc Careers, the job board for startup and VC roles.

## What they're looking for

- 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


Source: 1752vc Careers, https://www.1752.vc/careers/jobs/nominal-staff-machine-learning-engineer-time-series-and-statistical-methods/