Atlas AI
Founding Applied AI Engineer
San Francisco
Sponsorship not specifiedDetected 148 days ago
PythonNode.jsPostgreSQLRedisVector DatabasesAWSGCPLLMsRAGAI OrchestrationZendeskShopifyCollaboration
About the role
- 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
Responsibilities
- Our members are discerning, busy individuals who rely on Atlas to deliver insider access, curated service, and unmatched reliability.
- Our team brings deep experience from building and scaling iconic products at companies like Apple, Robinhood, and DoorDash.
- Our team comes from places like Shopify, Apple, Y Combinator startups - but what matters most here is what you build.
- 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.
Requirements
- Ideally, you have:
Nice to have
- 3+ years experience deploying and scaling cloud-based applications (AWS, GCP, or similar)
- Knowledge of retrieval-augmented generation (RAG), embeddings, and vector search (Pinecone, FAISS, or similar)
- Strong optimization skills, especially for low-latency, high-availability systems
- Product instincts - you think about the end experience, not just the technical specs
Skills
- Lightweight AI orchestration layers integrated with Zendesk, Slack, and our internal tools
Compensation
- Competitive salary and meaningful equity - you're helping build the foundation of Atlas.
Company info
- You'll help define how AI changes luxury travel, payments, and experiences for the most discerning customers in the world.
- 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.
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This listing is sourced directly from Atlas AI's careers page and normalized into a canonical job model.