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About the Role

Define how Nominal measures its agents for customers and internally, and build the path from evaluations to post-trained models when hardware requires them. Evaluations are prioritized, with post-training implemented when air-gapped deployment or cost efficiency dictates.

Responsibilities

Build eval suites for agents, the MCP tool layer, and the internal company agent, grounded in real hardware tasks.
Invent new benchmarks for agents working over physical engineering data.
Benchmark agents against frontier agents using MCP and identify areas of superiority.
Build the eval infrastructure, including datasets, model-based and human graders, and CI regression gates.
Convert evaluations into reward signals and training data, and lead post-training (fine-tuning, RL, distillation) for deployable models, including in air-gapped environments.
Set Nominal's strategy for measurement and model improvement in collaboration with hardware experts.

Requirements

8+ years in ML engineering or research, including production-level evaluations or post-training leadership.
Statistical rigor in designing non-deceptive evaluations and identifying significant differences.
Deep experience evaluating LLM or agent systems, including model-graded evaluations and their limitations.
Hands-on post-training experience: fine-tuning, RL from feedback, or distillation on real tasks.
Judgment to determine when to measure, train, or utilize improved prompts/tools.
Proven ability to set technical direction and elevate team performance.
Daily use of modern AI coding agents (Claude Code, Cursor, Codex) and a commitment to continuous learning.

Skills

ML engineering
ML research
Evals
Post-training
Statistical rigor
LLM evaluation
Agent system evaluation
Model-graded evaluations
Fine-tuning
RL from feedback
Distillation
AI coding agents

Location

Remote

Work Type

Full-time

Experience Level

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 with a connected test and operations platform used by 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.
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 and adapting to new AI tools daily.
The team's expertise spans data science, ML, distributed systems, search, and knowledge systems, with a focus on agent utility for engineers.

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.