Applied AI Engineer

About the Job

Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We stand out as an agency that’s deeply embedded in our clients’ recruitment operations. We partner directly with AI‑first startups building products powered by LLMs, generative AI, and intelligent automations; established tech companies scaling their ML infrastructure, recommendation systems, and data platforms;

and enterprise innovation teams integrating AI into traditional domains such as finance, healthcare, and logistics.

We collaborate directly with founders, CTOs, and Heads of AI in those themes who are driving the next wave of applied intelligence from model optimization to productized AI workflows.

They’re building a new medium for human connection: interactive digital minds that people can talk to, learn from, and be guided by. The internet gave us static profiles and infinite feeds; they create something profoundly different: living, interactive identities that carry your voice, judgment, and worldview into every conversation.

Mission:
Make human wisdom abundant, personalized, and discoverable; preserve legacies, unlock opportunity, and scale brilliance across generations.

Trusted by thousands of the world’s most brilliant thinkers, they’ve tripled revenue, users, and mind interactions in the past six months.

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.

Unlike traditional RAG systems that treat data as a static collection of documents, we build rich, hierarchical knowledge graphs that mirror how real experts think, capturing relationships, reasoning styles, and conceptual depth.

You’ll work at the intersection of graph systems, LLM reasoning, and scalable AI infrastructure, bringing together symbolic structure and neural intelligence to redefine how knowledge lives digitally.

• 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, accurate, and 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 evaluation 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…