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.
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
More about this role
Our mission is to accelerate how the world engineers new hardware. Nominal's connected test and operations platform powers the world's most advanced hardware programs and its most ambitious startups, from spacecraft, racecars, and autonomous vehicles to next-generation defense and energy programs. Our customers include Anduril, Shield AI, Hermeus, Albedo, Shinkei, and Pratt Miller Motorsports, as well as U.S. Navy and U.S. Air Force programs. Now we're expanding across the entire hardware lifecycle, building the foundation, AI-native applications, and agents that accelerate innovators' work and change what's possible to build.
We're backed by Sequoia, General Catalyst, Founders Fund, Lux Capital, and Lightspeed, and our team comes from SpaceX, Apple, Palantir, Anduril, Applied Intuition, and other leading companies.
We're the team behind Nominal's agents, AI-native applications, and MCP, and its forward-leaning AI bets. Our mission is to unlock the bottlenecks of the hardware lifecycle with AI. Our agents reason over physical reality, from high-rate telemetry and test campaigns to designs and simulations, where real test results are the ground truth their work is checked against....
Browse similar: AI jobs · AI startup jobs · Startup jobs