About the RoleWe'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.
