👾 About the Job
Company : AdsGency AI
📍 Location : Onsite / SF Bay Area Preferred
💼 Employment Type : Full-Time
🚀 About AdsGency AI
We’re AdsGency AI — a hacker-founded startup building autonomous, multi-agent systems to run digital advertising with zero human input .
Our product is a real-world application of AI agents that write ad copy, generate creatives, optimize budgets, launch campaigns , and learn from performance data — across Google, Meta, TikTok, and beyond. Think: LLM agents that ship ROAS .
We move like hackers, build in public, and write code that automates the world.
🧠 The Role – Member of Technical Staff, AI Agent (Multi-Agent Systems)
You’ll be one of our core engineers , helping us build a deeply technical and highly autonomous agentic platform. You’ll work across the full stack — from LLM orchestration and decision logic to real-world action-taking agents in production.
This is not a model research role. It’s not just prompt tuning. It’s real-world agent engineering:
LLMs with tools, memory, agency, and impact.
🔧 What You’ll Build
• 🤖 Agentic Ad Infrastructure – Build LLM agents that autonomously launch, optimize, and iterate ad campaigns
• 🧩 Multi-Agent Architectures – Use (or replace) frameworks like LangGraph, CrewAI, AutoGen, etc.
• 🧠 LLM-Tool Interfaces – Create agents that invoke APIs, run retrieval, self-reflect, and write to real systems
• 🛠 Agent Memory & Planning Systems – Integrate short-term and long-term memory, tools, tasks, and loops
• 🧪 Debugging & Evaluation Tools – Build in public, with trace-based evaluation for chain-of-thought reasoning
• 💥 Real-World Impact – These agents don’t just reason. They ship ads, generate revenue, and outperform humans.
💡 What We’re Looking For
✅ You’ve built with LangChain, CrewAI, AutoGPT, or your own agent framework
✅ You’ve used LLMs as coworkers (Cursor, Copilot, ChatGPT, Claude, Tabnine, etc.)
✅ You’ve shipped something weird, useful, or deeply technical with LLMs
✅ You’re not afraid to scrap the framework and write your own orchestration engine
✅ You’ve deployed agents in production, not just in Colab
✅ You like CLI tools, traces, logs, and think about agents as systems with state
🧠 Bonus Hacker Energy
• You’ve scraped something and built a data pipeline in a weekend
• You write shell scripts to automate your daily workflow
• You use git blame to learn and ship faster
• You’ve made PRs to open-source LLM tools or reverse-engineered APIs
• You believe memory, evaluation, and agent coordination are the real frontier
⚙️ Tech Stack We Love
• Python, FastAPI, LangChain, LangGraph, TypeScript, React
• OpenAI, Claude, HuggingFace, Ollama
• Vector DBs (Weaviate, Qdrant, LanceDB), Redis, Postgres
• Docker, AWS, Supabase, Railway
• GitHub > Slide decks
• Logs > Dashboards > Charts > Fancy reports
🎯 Who You Are
• Hacker > Engineer > Researcher
• 0-to-1 builder who ships scrappy before elegant
• Comfortable in open-ended, ambiguous environments
• Writes code in public, learns fast, fails fast, and debugs hard
• Interested in real-world agent deployment , not just theoretical chains
💰 What You Get
• Competitive salary + real equity
• Founding-level technical ownership
• Direct access to revenue-generating use cases (ads that work)
• Hacker-first culture with full engineering autonomy
• Early-stage engineering imprint on an AI-native company
• A team that writes code, not policies
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