{"id":2869299,"url":"https://alion.io/job/clera-staff-engineer-agentic-ai-9","title":"Staff Engineer, Agentic AI","company":{"id":2706,"name":"Clera","domain":"getclera.com","url":"https://alion.io/company/clera","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":80,"open_postings":30,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-10-25T05:49:26Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":160000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-10-19T17:23:50Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-25T13:18:43Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About the Role\nLead the development of agent intelligence that helps mechanical engineers complete multi-step workflows in desktop engineering software. You will combine hands-on engineering, technical leadership, and collaboration with user research and domain experts to improve real-world task completion and product value.\nWhat You'll Do\nOwn agent task success metrics, establish baselines, and systematically improve completion rates across engineering workflows.\n\nDesign and build reproducible evaluation infrastructure based on validated user stories, including tests for cost efficiency and failure modes.\n\nSet per-task token budgets and track the cost of completed workflows.\n\nInterview engineers with research and domain teammates, document their workflows, and translate validated stories into benchmarks.\n\nPrioritize workflow coverage based on customer value and technical feasibility.\n\nSet the agent architecture for tool calling, state management, error recovery, model routing, and context management.\n\nLead a small AI engineering team while writing production code, reviewing designs, and unblocking teammates.\n\nWork with product, integrations, and customers during proof-of-concept projects to align agent behavior with real usage.\n\nWhat We're Looking For\nAt least 7 years of software engineering experience, including 2 or more years building LLM-based agents that take real-world actions.\n\nStrong Python skills and deep experience with LLM application architecture, including model selection, retrieval, context management, orchestration, and tool calling.\n\nExperience building rigorous agent evaluation and benchmarking systems that measure task completion, cost, and failure modes.\n\nTechnical leadership experience on a small engineering team, with a track record of reviewing code and continuing to contribute production software.\n\nExperience shipping AI or LLM tools using proprietary engineering data or desktop engineering software.\n\nFamiliarity with function calling, tool APIs, tracing or observability, and evaluation tools.\n\nExperience with desktop automation or application APIs, such as COM, and a background in mechanical engineering software, CAD/CAE, PLM, or an adjacent domain.\n\nUnderstanding of deployment constraints on managed enterprise workstations.\n\nCompensation & Benefits\nBase salary: $160,000 to $250,000 USD annually. 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