About the role

As our founding ML engineer, your mission is to advance what's possible with AI agents. You'll be working at the frontier of how AI systems understand, remember, and reason about information.

You'll tackle challenges like:

• Contextual knowledge: Building systems that extract entities and relationships from unstructured data, enabling agents to traverse connections, surface insights, and provide context across organizations

• Smarter retrieval: Developing hybrid search strategies that combine semantic similarity, graph traversal, and other heuristics

• Memory that evolves: Creating systems that don't just store information but understand when facts change, determine what’s relevant, and intelligently forget what's no longer valid.

• Query understanding: Teaching our system to expand and rewrite queries, understand user intent, and retrieve information the user needs but didn't know to ask for

• Multi-modal pipelines: Improving how we parse, chunk, and represent complex documents, structured and unstructured data, tables, images, call recordings, hierarchical content.

This is a high-autonomy role where you'll own problems end-to-end: from researching approaches, to prototyping, to shipping production systems that handle millions of documents. You'll work directly with founders and have outsized impact on the product and company direction.

You don't need experience training models from scratch, but you need a deep understanding of LLMs, embeddings, NLP, and agentic architectures; as well as an innate intuition what actually moves the needle on agentic quality.

What you'll do

• Within 30 days, you'll own our core agentic query loop — the experience of serving our customer's AI agents the right context at the right time.

• Within 60 days, you'll work on our most ambitious projects: building a continuously updating, self-referential memory network; building procedural memory and memory for multi-user agents.

• Within 90 days, you'll productionize our memory features, present your work at conferences, and will already be working on the problems AI agents will be facing a year from now.

Requirements

• 3-5 years of experience in applied machine learning, AI engineering, or Natural Language Processing

• Fluency in Python

• Clarity of thought, excellent communication

• Ability to work on location in San Francisco

Compensation

• $150-250k base salary

• 0.5-1.5% equity

• $10,000 to build your dream work setup

• Health, dental, and vision coverage and generous health benefits

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.