Teams at Nominal

Embeds with hardware engineering teams to build agentic workflows and AI skills that perform engineering tasks, including requirements checks, test-plan analysis, and issue investigation. Demonstrates value to customers, identifies reusable patterns, and translates customer needs into product capabilities. Requires strong Python and data skills, customer-facing communication, modern AI coding-agent experience, and willingness to travel. Hardware, test, controls, forward-deployed, solutions engineering, and security-clearance experience are advantageous.

Design and build the platform powering Nominal’s AI agents, MCP, workflows, integrations, and customer-facing tools. Develop LLM features involving prompting, tool design, retrieval, observability, and evaluation. Collaborate directly with hardware engineers and customer programs, convert field insights into scalable capabilities, and own agent functionality from concept through production. The role also involves shaping technical direction and improving how agents support hardware testing and operations.

Lead development of AI agent products and AI-native applications for hardware engineering workflows. Prototype ideas rapidly, build products end to end, improve agent user experiences, establish interaction patterns, and lead technical direction across product engineering. Partner with design, product, and platform teams while working closely with engineers and users.