Staff Engineer — Agentic AI

About the Role

A well-funded, early-stage B2B SaaS company building AI agent infrastructure for mechanical engineering workflows is hiring a Staff Engineer — Agentic AI to own the core agent intelligence layer. This is a high-impact, senior technical leadership role reporting directly to the CTO. You'll sit at the intersection of applied agentic AI, user research, and product delivery — determining real-world value for Fortune 100 enterprise customers in the CAD, CAE, and PLM space.

You'll lead a small team of AI engineers, a user researcher, and domain expert contractors, acting as a player-coach who writes production code and sets technical direction.

• 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 — define the evaluation 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 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: tool‑calling strategies, state management, error recovery, model routing, and context management.

• Set technical direction, review architecture decisions, unblock the team, and raise the engineering bar across a team of 3–6 engineers.

• Collaborate cross‑functionally with integrations, product, and customers during POCs to align agent behavior with real‑world usage.

Must-haves:

• 7+ years in software engineering, including at least 2 years building agentic LLM-based systems that act in the real world (multi-step workflows, tool‑calling, failure handling, cost constraints).

• 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, cost efficiency, and failure mode analysis; familiarity with SWE‑bench, GAIA, or τ‑bench.

• Proven track record of shipping AI systems with measurable outcomes, not just demos.

• Proficiency in Python and the LLM tooling ecosystem (function calling, tool use APIs, tracing/observability tools such as Logfire or Lang Smith, evaluation frameworks).

• Experience leading a small technical team (3–6 engineers): setting direction, performing code reviews, and driving architecture decisions.

Nice-to-haves:

• 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 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.

Note: Visa sponsorship is not available for this role.

• Salary: $160,000 – $250,000 USD annually

• Early-stage equity

• Direct line to executive leadership and outsized scope of impact

This is an on-site role based in San Francisco, CA
. Candidates must be willing to work from the office.