About the RoleThis is a senior technical leadership role at the heart of an AI-native engineering software company, owning the core agent intelligence layer that turns mechanical engineers' intent into reliable, cost-efficient multi-step workflows across complex desktop engineering tools. You'll report directly to the CTO and serve as the technical lead for a small team of AI engineers, a user researcher, and domain expert contractors. The work you do here will define real-world product value for enterprise customers.
What You'll Do- Lead development of the agent intelligence layer that executes multi-step workflows across CAD, simulation, and PLM software.
- Own the full product loop - from user story definition to implementation to benchmarking against real engineering workflows.
- Drive agent task success rate by defining evaluation frameworks, establishing baselines, and iterating on performance.
- Set and enforce per-task token budgets and track cost per completed workflow to ensure commercial viability.
- Design rigorous, reproducible evaluation infrastructure grounded in validated user stories - think SWE-bench-level rigor applied to engineering workflows.
- Lead user story mapping and validation through direct interviews and collaboration with domain experts.
- Translate validated user stories into testable evals, closing the loop between user research and benchmarking.
- Own agent architecture decisions: tool-calling strategies, state management, error recovery, model routing, and context management.
- Act as a player-coach - write production code, review designs, unblock the team, and raise the engineering bar.
- 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 and shipping real-world agentic LLM systems (tool calling, multi-step workflows, failure recovery, cost control).
- Deep experience with LLM application architecture: model selection, context/window management, retrieval strategies, tool-calling frameworks, and orchestration patterns.
- Strong evaluation and benchmarking instincts for agentic systems - task completion rates, cost efficiency, failure mode analysis; familiarity with benchmarks such as SWE-bench, GAIA, or -bench is a plus.
- Proven track record of shipped AI systems with measurable outcomes - not just demos or prototypes.
- Strong Python skills and hands-on familiarity with the LLM tooling ecosystem (function calling, tool use APIs, tracing/observability tools, evaluation frameworks).
- Technical leadership experience setting direction for small teams (3-6 engineers) and performing meaningful code review and architecture decisions.
- Hands-on background with mechanical engineering software - CAD/CAE/PLM or simulation tooling (e.g. Siemens NX/NXOpen, Teamcenter, CATIA, Creo, SolidWorks, Ansys, Abaqus, or similar) - either as a builder of these tools or as a power user inside an engineering or manufacturing org.
- Experience shipping AI/LLM tooling on top of proprietary engineering data or desktop engineering software (e.g. an agent or MCP server over CAD/PLM APIs, RAG over engineering repos or schematics).
- Familiarity with enterprise deployment constraints, including behavior on locked-down corporate workstations.
- Experience with desktop automation or programmatic control of applications (COM or similar) is a strong plus.
- Published work, open-source contributions, or benchmark contributions in agentic AI is a plus.
Compensation & BenefitsSalary range:
$160,000 - $250,000 USD annually. Visa sponsorship is not available for this role.
LocationOn-site in
San Francisco, California, USA.
