Clera - Staff Engineer

Remote - San Francisco, California, United States$160k - $250k+ Equity3mo ago

Remote Staff NA Artificial Intelligence Staff Engineer CTO Python Coaching Performance Reviews Reporting

Requirements

• 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. • Strongly Preferred: • Strongly Preferred: • 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.

Responsibilities

• 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.

Benefits

• Salary: $160,000 – $250,000 USD annually, depending on experience. • Equity participation in an early-stage, Series A company. • Note: Visa sponsorship is not available for this role. • Note: • Location • On-site in San Francisco, CA, United States. • On-site • This is not a remote role.