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About the Role

As a Staff Machine Learning Engineer, Time Series & Statistical Methods, you'll provide agents with essential numerical tools, as LLMs alone cannot effectively reason over millions of sensor samples. You will define Nominal's machine learning strategy for hardware data, encompassing both classical methods and deep learning.

Responsibilities

Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry.
Package these methods as agent tools for real feature engineering and statistical analysis.
Develop signal-processing and statistical methods (frequency analysis, trend fitting, run-to-run comparison) robust to noisy, real-world data.
Partner with evals teams to measure method effectiveness for engineers.
Set the technical direction and roadmap for ML at Nominal, evaluating the investment in learned models versus classical ones.

Requirements

8+ years in applied ML or statistics with production systems on time-series or sensor data.
Deep classical grounding in statistics, signal processing, anomaly detection, and forecasting.
Hands-on deep learning experience with transformers, embeddings, and representation learning for sequences and sensor data.
Strong software engineering skills, with methods shipping as reliable, tested code.
Judgment to discern when simple statistical tests are preferable to neural networks.
Proven track record of setting technical direction and raising the bar for engineering teams.
Daily use of modern AI coding agents (e.g., Claude Code, Cursor, Codex) and a commitment to continuous learning and improvement.

Skills

Time Series Analysis
Statistical Methods
Anomaly Detection
Change-Point Detection
Forecasting
Signal Processing
Frequency Analysis
Trend Fitting
Run-to-Run Comparison
Deep Learning
Transformers
Embeddings
Representation Learning
Software Engineering
AI Coding Agents

Location

Remote

Work Type

Full-time

Experience Level

Staff
8+ years

Benefits

100% coverage of medical, dental, and vision insurance
Unlimited PTO and sick leave
Free lunch, snacks, and coffee
Professional Development Stipend
Annual company retreat

About the Company

Nominal's mission is to accelerate hardware engineering through its connected test and operations platform, serving advanced hardware programs and startups.
Customers include Anduril, Shield AI, Hermeus, Albedo, Shinkei, and Pratt Miller Motorsports, as well as U.S. Navy and U.S. Air Force programs.
The company is expanding across the hardware lifecycle, building AI-native applications and agents.
Nominal is backed by investors including Sequoia, General Catalyst, Founders Fund, Lux Capital, and Lightspeed.
The team comprises individuals from leading companies such as SpaceX, Apple, Palantir, Anduril, and Applied Intuition.
The Hardware Intelligence team develops Nominal's agents, AI-native applications, and MCP, focusing on unlocking hardware lifecycle bottlenecks with AI.
Their agents reason over physical reality, using real test results as ground truth.
The team is collaborative, iterative, high-agency, human-centered, and customer-focused, utilizing the newest AI tools daily.

Equal Opportunity

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
ITAR Requirements: Applicants must be a U.S. citizen or national, U.S. lawful permanent resident, Refugee, or Asylee, or eligible for required U.S. Department of State authorizations.