Job Title

Build Agents. Ship Intelligence. Define the Category. The Mission Search Atlas hit $32M ARR bootstrapped . No VC. No safety net. Just product that works. Now we're building agentic marketing - AI systems that don't report data, they execute strategies autonomously . Millions of pages crawled. Decisions made in milliseconds. Fortune 500s trusting our agents with their growth. We need a Technical PM who prototypes faster than most teams ship. You don't write tickets about AI. You build agents in Cursor, validate with SQL, and deploy to production. What Winning Looks Like Week 2: You've shipped a prototype agent in Claude Code that fixes technical SEO issues autonomously. It works. It's rough. It's real. Month 3: Your agent is in production serving enterprise customers. You've defined the evaluation framework, the latency budget, the failure modes. Engineers trust your specs because you've validated the approach yourself. Month 6: You're architecting the next generation of autonomous marketing systems. Other PMs study your playbooks. Your Playground You'll own one core agentic system end-to-end: OTTO - Our autonomous SEO agent. Crawls sites. Diagnoses issues. Executes fixes. No humans in the loop. Content Intelligence - Semantic engines that generate, optimize, and publish content autonomously. Brand Knowledge Graphs - AI systems that build, maintain, and leverage entity relationships at scale. The Work Architect Agent Behavior Design reasoning chains, tool use patterns, and reflection mechanisms. Your agents don't just respond - they think, act, verify. Write specs that include prompt architectures, evaluation datasets, and edge case handling. Engineers review your PRDs for technical depth. Build working prototypes in Cursor, Claude Code, or raw Python to prove viability before engineering investment. Master the Data Layer Query terabyte-scale datasets in ClickHouse and PostgreSQL. Window functions, complex joins, query optimization - you don't delegate this. Use Python (pandas, SQL Alchemy) to analyze agent performance, identify failure patterns, and propose improvements. Design evaluation pipelines: hallucination detection, citation verification, confidence scoring, human-in-the-loop triggers. Figma prototypes that handle millions of data points without overwhelming users. Maximum insight per pixel. Real-time streaming updates. Interactive agent explanations. Interfaces that make complexity feel inevitable. Work shoulder-to-shoulder with engineers on React/TypeScript implementations. You don't hand off designs; you co-build. Drive 10x Velocity Lead standups that unblock, not update. Sprint planning that commits aggressively and delivers completely. QA in staging with engineering rigor. Test edge cases. Validate reasoning chains. No "throw it over the wall." Maintain 48-hour prototype-to-feedback cycles. Ship weekly, learn daily. You You've shipped AI products that users trust - not demos, production systems. 5+ years in technical product management. 2+ years building AI/ML systems in production environments. SQL fluency at scale. You write complex queries, optimize performance, debug data pipelines personally. Python proficiency for analysis, scripting, and prototyping. You read code, write scripts, and validate technical approaches without engineering handholding. AI/ML depth - you understand ReAct, tool use, reflection, chain-of-thought. You've implemented these patterns, not just read about them. Prototyping velocity - Cursor, Claude Code, Jupyter, Streamlit. You build to learn in hours, not weeks. Design craft - Figma at high fidelity. You care about interaction details, information density, flow states. Engineering fluency - Git workflows, API design, basic React/TypeScript. You review PRs, not just PRDs. Bonus: Built autonomous agents (not chatbots). SEO/MarTech domain expertise. Frontend or data engineering background. Vector DB/RAG at scale. The Stack Agents: Custom architectures, LLM tool use, reasoning chains, evaluation frameworks Data: PostgreSQL, ClickHouse, Elasticsearch, terabytes of real-time search data Design: Figma, Figma Make, Lovable The Deal Compensation Base: $180K-$250K Benefits: Fully covered medical (Aetna), 99% dental/vision, unlimited PTO, paid parental leave, $100/quarter dev budget, company-paid Lasik (2-year vest), pet insurance (Lemonade, 2 pets), 401(k) via Deel The Opportunity Hybrid in San Francisco. Energy, serendipity, rapid iteration - this doesn't happen fully remote. We ship weekly. We prototype daily. We measure everything. No PMs who don't vibe code. No features without evaluation frameworks. No roadmap maintenance - only category creation. The Company Search Atlas powers autonomous marketing for Fortune 500s and high-growth startups: OTTO SEO - Fully autonomous technical optimization Content Genius - Semantic AI content generation and publishing Site Explorer - Real-time search intelligence and monitoring BrandVault - AI-driven knowledge graphs GBP Galactic - Local SEO automation Smart Ads - Autonomous PPC management LLM Visibility - AI search analytics and optimization Inc. 5000. Nevada's #1 Small Business Workplace. Great Place to Work Certified. Bootstrapped. Profitable. Growing fast. Building the future from San Francisco. No cover letters. No generic applications. Just evidence of craft. $32M ARR. Bootstrapped. Category-defining. San Francisco. Search Atlas is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all team members. #J-18808-Ljbffr