Catalyst Labs
Overview
Join to apply for the
Applied AI Engineer
role at
Catalyst Labs . About Catalyst Labs
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, 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. We take pride in facilitating conversations that align with your technical expertise, creative problem-solving mindset, and long-term growth trajectory in the evolving world of intelligent systems. The Role
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 not just facts, but 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. What You’ll Do
Knowledge Graph & Mind Architecture : Design and evolve the graph-based architecture that models a mind’s reasoning, associations, and conceptual hierarchies. RAG & Context Innovation : 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. What You Bring
Core Expertise 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. Preferred / Bonus Experience with graph databases or graph-based retrieval (Neo4j, ArangoDB, 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 are a great fit if
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. Benefits
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.
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Join to apply for the
Applied AI Engineer
role at
Catalyst Labs . About Catalyst Labs
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, 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. We take pride in facilitating conversations that align with your technical expertise, creative problem-solving mindset, and long-term growth trajectory in the evolving world of intelligent systems. The Role
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 not just facts, but 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. What You’ll Do
Knowledge Graph & Mind Architecture : Design and evolve the graph-based architecture that models a mind’s reasoning, associations, and conceptual hierarchies. RAG & Context Innovation : 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. What You Bring
Core Expertise 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. Preferred / Bonus Experience with graph databases or graph-based retrieval (Neo4j, ArangoDB, 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 are a great fit if
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. Benefits
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.
#J-18808-Ljbffr