Build Agents. Ship Intelligence. Define the Category.

Location: San Francisco, CA (Hybrid)

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.

This is zero-to-one product craft at scale. San Francisco. In-person energy. Ship-or-die velocity.

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.

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.

Craft Dense Interfaces

• 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
AI/ML: OpenAI, Anthropic, Groq, self-hosted models, custom eval pipelines
Prototyping: Cursor, Claude Code, Jupyter, Streamlit, raw Python
Design: Figma, Figma Make, Lovable
Engineering: GitLab, Next.js, React, TypeScript, Python, Kubernetes

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 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.