This is a senior technical leadership role at the heart of an early-stage AI software company building intelligent agents for hardware engineers. You will own the core agent intelligence layer that turns engineers' intent into reliable, cost-efficient multi-step workflows across desktop CAD, simulation, and PLM tools. Reporting directly to the CTO, the work you do here determines the product's real-world value to enterprise customers.
Lead development of the agent intelligence layer that executes multi-step workflows across complex desktop engineering software.
Serve as technical lead for a small team of AI engineers, a user researcher, and domain expert contractors.
Own the full product loop: define agent capabilities from user stories, build implementations, and benchmark against real workflows.
Drive agent task success rate by defining evaluation frameworks, establishing baselines, and iterating on completion metrics.
Set and enforce per-task token budgets and track cost per completed workflow to ensure commercial viability.
Build rigorous, reproducible evaluation infrastructure grounded in validated user stories.
Lead user story mapping and validation through interviews and close collaboration with domain experts.
Translate validated user stories into testable evals, closing the loop between user research and benchmarking.
Own agent architecture decisions including tool-calling, state management, error recovery, model routing, and context management.
Act as a player-coach: write production code, review designs, unblock the team, and raise engineering standards.
Collaborate cross-functionally with integrations, product, and customers during POCs to align agent behavior with real-world usage.
7+ years of software engineering experience, including at least 2 years building LLM-based agents that take real-world actions.
Deep experience designing LLM application architectures: model selection, context and window management, retrieval, tool calling, and orchestration patterns.
Hands-on experience shipping AI or LLM tooling on top of proprietary engineering data or desktop engineering software (for example, agents or MCP servers over CAD, PLM, or simulation platforms); general-purpose chatbot or web-app RAG work alone does not qualify.
Strong Python proficiency and familiarity with LLM function calling, tool APIs, observability and tracing, and evaluation frameworks.
Proven ability to build evaluation and benchmarking frameworks measuring task completion, cost efficiency, and failure modes.
Technical leadership experience: setting direction for small teams of 3 to 6 engineers and performing meaningful code review while continuing to write production code.
Experience with desktop automation or programmatic control of applications such as COM or similar interfaces.
Domain background in mechanical engineering, CAD, CAE, PLM, or an adjacent engineering-software field.
Familiarity with enterprise deployment constraints on locked-down corporate workstations.
Track record contributing to public benchmarks, publications, or open-source agentic AI projects is a plus.
Base salary range: $160,000 to $250,000 USD annually, plus equity. Visa sponsorship is not available for this role.
On-site in San Francisco, California, United States.
Job details are sourced from the employer's original posting.
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