Catalyst Labs
About Us
Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We partner directly with AI‑first startups building products powered by LLMs, established tech companies scaling their ML infrastructure, 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 to drive the next wave of applied intelligence from model optimization to productized AI workflows.
Client Overview Our client is reimagining how knowledge is shared. They are building a new medium for human connection: interactive digital minds that people can talk to, learn from, and be guided by. The mission is bold and generational in scope: 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 and backed by top venture capital, they are scaling rapidly toward the next frontier of AI‑driven human expression.
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.
Role Overview As an Applied AI Engineer, you will help architect the foundation of next‑generation Mind Architecture, a 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 will 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
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.
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 evaluation 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.
Fit
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 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.
Other Roles in Applied AI / ML in the US
Applied AI / ML engineer focusing on LLMs, and Document processing: https://www.careers-page.com/catalyst-labs
#J-18808-Ljbffr
Client Overview Our client is reimagining how knowledge is shared. They are building a new medium for human connection: interactive digital minds that people can talk to, learn from, and be guided by. The mission is bold and generational in scope: 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 and backed by top venture capital, they are scaling rapidly toward the next frontier of AI‑driven human expression.
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.
Role Overview As an Applied AI Engineer, you will help architect the foundation of next‑generation Mind Architecture, a 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 will 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
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.
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 evaluation 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.
Fit
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 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.
Other Roles in Applied AI / ML in the US
Applied AI / ML engineer focusing on LLMs, and Document processing: https://www.careers-page.com/catalyst-labs
#J-18808-Ljbffr