Staff Engineer - Agentic AI

• The company serves Fortune 100 hardware engineering customers and is backed by notable investors. You'll join a small, senior team and have a direct line to executive leadership. The role is on-site in San Francisco, CA.
• Lead development of the core agent intelligence layer executing multi-step workflows across complex desktop engineering software (CAD, CAE, PLM).
• Report to the CTO and 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 — define the eval framework, establish baselines, and systematically improve completion metrics.
• Set and enforce per-task token budgets; track cost per completed workflow to ensure commercial viability.
• Build rigorous, reproducible evaluation infrastructure grounded in validated user stories (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 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 engineering standards.
• Collaborate cross-functionally with integrations, product, and customers during POCs to align agent behavior with real-world usage.
• 7+ years in software engineering, including at least 2 years building agentic LLM-based agents that act in the real world (tool-calling, multi-step workflows, failure handling, cost constraints).
• Deep experience designing LLM application architectures: model selection, context/window management, retrieval strategies, tool-calling frameworks, and orchestration patterns.
• Strong evaluation and benchmarking instincts for agentic systems — task completion, cost efficiency, failure mode analysis; familiarity with SWE-bench, GAIA, or τ-bench.
• Proven track record shipping AI systems with measurable outcomes (e.g., agent task success rate, cost efficiency) — not just demos.
• Strong Python skills and hands-on experience with LLM tooling (function calling, tool use APIs, tracing/observability tools such as Logfire or LangSmith, evaluation frameworks).
• Experience leading a small technical team (3–6 engineers): setting direction, performing code reviews, driving architecture decisions.
• Experience with desktop automation, COM, or programmatic control of applications (beyond web APIs).
• Background in mechanical engineering, CAD/CAE, PLM, or adjacent industries.
• Familiarity with enterprise deployment constraints — agent behavior on locked-down corporate workstations.
• Published work or open-source contributions in agentic AI systems.
• Experience building or contributing to public benchmarks for AI agents.
• On-site in San Francisco, CA, United States.

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