Technical Product Manager AIML

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 were building agentic marketing - AI systems that dont 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 dont 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 -person energy. Ship-or-die velocity.

What Winning Looks Like

Week 2: Youve shipped a prototype agent in Claude Code that fixes technical SEO issues autonomously. It works. Its rough. Its real.

Month 3: Your agent is in production serving enterprise customers. Youve defined the evaluation framework the latency budget the failure modes. Engineers trust your specs because youve validated the approach yourself.

Month 6: Youre architecting the next generation of autonomous marketing systems. Other PMs study your playbooks.

Youll 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 dont 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 dont 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 dont 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

Youve 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. Youve 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 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 doesnt happen fully remote.

• We ship weekly. We prototype daily. We measure everything.

• No PMs who dont 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. Nevadas #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.

Must be currently authorized to work in the US without future sponsorship.