Applied AI Engineer

Location

Jackson Square, San Francisco

Fully on-site, 5 days a week

Above-market base + equity + benefits

Sponsorship available only for candidates already in the United States

As an Applied AI Engineer, you’ll help architect the foundation of next-generation Mind Architecture, the system that powers how digital minds learn, reason, and express individuality.

• Design and evolve the graph-based architecture that models a mind’s reasoning, associations, and conceptual hierarchies.
• Develop novel embeddings and contextual retrieval methods that make each mind distinct not just accurate, but alive.
• Build next-generation retrieval-augmented generation systems that integrate structured graph reasoning with neural retrieval.
• Implement and refine LLM evaluation frameworks to ensure each mind improves over time in quality, consistency, and individuality.

• Push the performance of frontier LLMs through advanced prompt engineering, dynamic context shaping, and evaluation-driven iteration.
• Architect efficient, low-latency inference pipelines for thousands of simultaneous mind interactions.

• Bridge ML systems and product engineering shipping AI-powered features that shape user experience directly.
• Translate abstract research ideas into robust, scalable production systems powering thousands of live digital minds.

• Define success metrics for mind quality advancing tone, style transfer, and reasoning fidelity.
• Build systems that continuously refine a digital mind’s authenticity and expressiveness.

• 4-6+ years of experience in AI or ML engineering, with at least 12 years hands‑on with LLMs in production.
• Proven track record of building and deploying ML‑powered systems that directly impact end‑user experience.
• Strong Python expertise, including asynchronous programming, event loop optimization, and performance tuning.
• Experience with retrieval‑augmented generation (RAG), context engineering, and LLM eval frameworks.
• Deep practical understanding of how LLMs reason, how to structure inputs, and how to measure improvement.

• Experience with graph databases or graph-based retrieval (Neo4j, Arango
  
  DB, or custom‑built graph layers).
• Background in document understanding, embeddings, or multimodal context assembly.
• Familiarity with AWS, distributed systems, and scalable backend architecture.
• Hands‑on experience fine‑tuning LLMs or building adapters for tone/style adaptation.

• You thrive on hard problems and are motivated by impact over title.
• You’re comfortable building at the intersection of research, product, and systems engineering.
• You have an engineer’s rigor, a creator’s imagination, and a philosopher’s curiosity.

• Unlimited Learning Stipend: books, courses, or conferences of your choice.
• Comprehensive Health, Dental, and Vision coverage.
• 401(k) via Human Interest.
• Relocation assistance to San Francisco (if applicable).
• A creative, high‑agency culture that celebrates curiosity and deep thinking.