Sr. Applied Scientist (AI/ML), New Venture

At Sanity.io, we’re building the future of AI-powered Content Operations. Our AI Content Operating System gives teams the freedom to model, create, and automate content the way their business works, accelerating digital development and supercharging content operations efficiency. Companies like SKIMS, Figma, Riot Games, Anthropic, COMPLEX, Nordstrom, and Morningbrew are using Sanity to power and automate their content operations.

As part of our new venture, your work will center on addressing one of AI’s toughest problems: how to help machines truly understand and use human-created content. You’ll build systems that structure and enrich large volumes of information to enable AI agents and LLMs to access the right context at the right time. This means designing and developing tools and pipelines that shape, structure, and connect information and content in innovative ways, and creating new methods to ensure AIs reflect the most accurate, authentic, and up-to-date representation of a business, its brand, products, and knowledge base.

As a Senior Applied Scientist you'll lead the innovation and research that powers our next generation of AI capabilities. You'll be the technical visionary who invents novel algorithms and methodologies for how AI systems understand and utilize content, then takes those innovations all the way to production. Your work will span from deep research into LLM behavior and evaluation to designing cutting-edge architectures and knowledge graph systems. You'll join a small, cross-functional team where you'll lead the AI technical direction, exploring new approaches independently and bringing breakthrough ideas back to the team for collaborative implementation. If you're passionate about pushing the boundaries of what's possible with LLMs, love the challenge of inventing new solutions to hard problems, and can bridge the gap between research and production, we'd love to have you on the team.

What you will do:

• Lead research and development of novel approaches for content understanding, structuring, and retrieval to enable AI agents and LLMs to access optimal context.

• Research and prototype novel approaches to knowledge graph construction, entity extraction, and relationship mapping for AI-powered content systems.

• Stay at the forefront of LLM research, understanding model behaviors, limitations, and capabilities to inform system design decisions.

• Develop evaluation frameworks and metrics to measure and improve the performance of AI systems, including designing experiments to test new methodologies.

• Design and conduct experiments to validate new approaches, analyzing results and iterating based on findings.

• Keep up with the latest research in LLMs, RAG, knowledge graphs, and related fields, translating cutting-edge academic work into practical applications.

• Collaborate closely with data engineers and full stack engineers to integrate innovative AI solutions into the product.

About you:

• Based in the San Francisco Bay Area and able to work at least 2 days per week in our San Francisco office.

• 5+ years of experience in software engineering, applied AI/ML research, or a related technical field, with a strong track record of taking ideas from concept to production.

• 2+ years of hands-on experience working with large language models (LLMs) or building AI-powered applications.

• Deep expertise in large language models (LLMs), including understanding of model architectures, training methods, fine-tuning, prompt engineering, and evaluation techniques.

• Strong background in Natural Language Processing (NLP), information retrieval, and semantic understanding.

• Experience with knowledge graphs, graph databases, entity extraction, and relationship mapping.

• Strong software engineering skills with the ability to write production-quality code and take research prototypes to scalable implementations.

• Ability to work independently on exploratory research while also collaborating effectively with cross-functional teams.

• Strong analytical and experimental mindset - you love designing experiments, analyzing results, and iterating based on data.

• Excellent problem-solving skills and intellectual curiosity about how things work under the hood.

• Strong communication skills and ability to explain complex technical concepts clearly to both technical and non-technical audie