Catalyst Labs
About Catalyst Labs
Catalyst Labs is a talent agency delivering applied AI, machine learning, and data science solutions to startups, established tech firms, and enterprise innovation teams. We partner with founders, CTOs, and heads of AI to build models that power next‑generation digital minds. The Role
As an Applied AI Engineer you will architect the mind architecture that powers how digital minds learn, reason, and express individuality. You will work at the intersection of graph systems, large language models, and scalable AI infrastructure. Responsibilities
Design and evolve the graph‑based architecture that models a mind’s reasoning, associations, and conceptual hierarchies. Develop novel embeddings and contextual retrieval methods to make each mind distinct, accurate, and alive. Build retrieval‑augmented generation systems that integrate structured graph reasoning with neural retrieval. Implement and refine LLM evaluation frameworks to ensure continuous improvement of mind quality. Push frontier LLM performance 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 to ship AI‑powered features that shape user experience. Define success metrics for mind quality and build systems that continuously refine authenticity and expressiveness. Qualifications
4‑6+ years of AI/ML engineering experience with 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. Preferred
Experience with graph databases or graph‑based retrieval (Neo4j, ArangoDB, or custom 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. Fit
Thrives on hard problems and is motivated by impact over title. Comfortable building at the intersection of research, product, and systems engineering. Engineer's rigor, creator’s imagination, and philosopher’s curiosity. Benefits
Unlimited Learning Stipend. Comprehensive Health, Dental, and Vision coverage. 401(k) via Human Interest. Relocation assistance to San Francisco (if applicable). High‑agency culture that celebrates curiosity and deep thinking. 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
#J-18808-Ljbffr
Catalyst Labs is a talent agency delivering applied AI, machine learning, and data science solutions to startups, established tech firms, and enterprise innovation teams. We partner with founders, CTOs, and heads of AI to build models that power next‑generation digital minds. The Role
As an Applied AI Engineer you will architect the mind architecture that powers how digital minds learn, reason, and express individuality. You will work at the intersection of graph systems, large language models, and scalable AI infrastructure. Responsibilities
Design and evolve the graph‑based architecture that models a mind’s reasoning, associations, and conceptual hierarchies. Develop novel embeddings and contextual retrieval methods to make each mind distinct, accurate, and alive. Build retrieval‑augmented generation systems that integrate structured graph reasoning with neural retrieval. Implement and refine LLM evaluation frameworks to ensure continuous improvement of mind quality. Push frontier LLM performance 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 to ship AI‑powered features that shape user experience. Define success metrics for mind quality and build systems that continuously refine authenticity and expressiveness. Qualifications
4‑6+ years of AI/ML engineering experience with 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. Preferred
Experience with graph databases or graph‑based retrieval (Neo4j, ArangoDB, or custom 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. Fit
Thrives on hard problems and is motivated by impact over title. Comfortable building at the intersection of research, product, and systems engineering. Engineer's rigor, creator’s imagination, and philosopher’s curiosity. Benefits
Unlimited Learning Stipend. Comprehensive Health, Dental, and Vision coverage. 401(k) via Human Interest. Relocation assistance to San Francisco (if applicable). High‑agency culture that celebrates curiosity and deep thinking. 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
#J-18808-Ljbffr