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 and partner directly with AI-first startups, established tech companies, and enterprise innovation teams building products powered by LLMs, generative AI, and intelligent automations. Location & Work Type
Jackson Square, San Francisco Fully on-site, 5 days a week Compensation
Above-market base + equity + benefits Visa
Sponsorship available only for candidates already in the United States The Role
As an Applied AI Engineer, you’ll help architect the foundation of next-generation Mind Architecture that powers how digital minds learn, reason, and express individuality. 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. Responsibilities
Knowledge Graph & Mind Architecture
– 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, alive, and accurate. 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. Applied AI & Performance
– 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. Systems & Product Integration
– 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. Evals & Personalization
– 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. Qualifications
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 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
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 and partner directly with AI-first startups, established tech companies, and enterprise innovation teams building products powered by LLMs, generative AI, and intelligent automations. Location & Work Type
Jackson Square, San Francisco Fully on-site, 5 days a week Compensation
Above-market base + equity + benefits Visa
Sponsorship available only for candidates already in the United States The Role
As an Applied AI Engineer, you’ll help architect the foundation of next-generation Mind Architecture that powers how digital minds learn, reason, and express individuality. 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. Responsibilities
Knowledge Graph & Mind Architecture
– 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, alive, and accurate. 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. Applied AI & Performance
– 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. Systems & Product Integration
– 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. Evals & Personalization
– 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. Qualifications
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 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