About Atlas

Atlas is the concierge and credit card built for those who expect more — unlocking coveted access across dining, travel, and lifestyle while making spending seamless and effortless.

Our members are discerning, busy individuals who rely on Atlas to deliver insider access, curated service, and unmatched reliability.

In just 12 months, we've scaled to over $X00,000,000 in spending volume, growing double digits month-over-month — organically, without paid marketing.

We're backed by world-class investors and have multiple years of runway ahead. Our team brings deep experience from building and scaling iconic products at companies like Apple, Robinhood, and DoorDash.

Today, we're a small, high-performing team of around 35 people based between New York and San Francisco. We value in-person collaboration and expect you to join us in our SF office (FiDi) at least three days a week.

The Role

We’re looking for an applied AI engineer to help us build the next generation of Atlas — starting with an AI-powered travel and hotel concierge used by some of the most influential people in the world.

You’ll design and ship real-world AI systems that personalize recommendations, automate complex requests, and rethink how payments, dining, and travel can work at the highest level.

This is a rare opportunity to work on meaningful, user-facing AI products inside a small, world-class team.

What You'll Build

• AI recommendation and booking flows (hotels, dining, flights)
• Retrieval-augmented generation (RAG) systems for dynamic hotel and travel searches
• Preference engines that learn and evolve from member interactions
• Lightweight AI orchestration layers integrated with Zendesk, Slack, and our internal tools
  

What We’re Looking For

We believe determined and passionate people can adapt to any tech stack. Ideally, you have:

• 5+ years experience developing software in modern environments (Python, Node.js, Go, or similar)
• 3+ years experience deploying and scaling cloud-based applications (AWS, GCP, or similar)
• Deep experience integrating LLMs (OpenAI, Anthropic, Hugging Face) and building production AI systems
• Knowledge of retrieval-augmented generation (RAG), embeddings, and vector search (Pinecone, FAISS, or similar)