Staff Engineer — Agentic AI

About the Role

We're hiring a senior technical leader to own the core agent intelligence that turns engineers' intent into reliable, cost-efficient multi-step workflows across desktop engineering tools. This role sits at the intersection of applied agentic AI, user research, and product delivery and will determine the product's real-world value to enterprise customers.

You'll report to the CTO and serve as technical lead for a small team of AI engineers, a user researcher, and domain expert contractors in an early-stage, high-impact environment (Series A, Fortune 100 customers, direct line to leadership).

What You'll Do

• Lead development of the core agent intelligence layer that executes multi-step workflows across complex desktop engineering software.

• 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 to improve 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 and close the loop between 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.

What We're Looking For

• 7+ years in software engineering, including at least 2 years building agentic LLM-based agents that act in the real world.

• Deep experience designing LLM application architectures, including model selection, context/window management, retrieval, and orchestration patterns.

• Proven ability to build evaluation and benchmarking frameworks measuring task completion, cost efficiency, and failure modes.

• Technical leadership experience setting direction for small teams (3–6 engineers) and performing meaningful code review.

• Strong Python skills and familiarity with LLM tooling (function calling, tool APIs, observability/tracing, evaluation frameworks).

• Experience with desktop automation or programmatic control of applications (COM or similar).

• Nice to have: Domain experience in mechanical engineering, CAD/CAE, PLM, or adjacent industries.

• Nice to have: Understanding of enterprise deployment constraints on locked-down corporate workstations.

• Nice to have: Track record contributing to public benchmarks, publications, or open-source agentic AI projects.