We’re an early-stage AI company building a new infrastructure layer for AI systems: enabling models and agents to reliably interact with the real-world data that matters.

Today, most enterprise data (~80%+) is unstructured and multimodal—documents, tables, charts, images—yet existing systems (LLMs, OCR, RAG pipelines) fail to process it reliably.

We’re solving this by building state-of-the-art vision-language models and systems that transform complex, real-world inputs into structured, machine-readable representations that downstream models can actually reason over.

Our models are already deployed in production across high-stakes domains (finance, legal, healthcare) where accuracy matters, and are outperforming existing approaches on real-world benchmarks.

We believe this layer—making messy reality legible to AI systems—is one of the most important unsolved problems in the stack.

The Role

We’re hiring a Founding ML Researcher to work directly with the founders to define and build the core research agenda.

This is not a typical applied ML role. You’ll be operating at the intersection of:

Multimodal foundation models
Vision-language reasoning
Structured generation / parsing
Reliability and determinism in AI systems

You’ll have end-to-end ownership—from first-principles research → model design → training → production deployment.

What You’ll Work On

Designing novel architectures for multimodal understanding (documents, tables, layouts, graphs)
Pushing beyond standard LLM paradigms into structure-aware and layout-aware models
Improving factuality, determinism, and reliability in model outputs
Building systems that combine:
Vision models
Language models
Structured decoding / constrained generation
Developing evaluation frameworks for real-world correctness (not just benchmark scores)
Shipping research directly into production systems used by real customers

Who This Is For

We’re specifically looking for researchers currently at (or competitive with):

Frontier labs (e.g. Anthropic, OpenAI, DeepMind, Meta, etc.)
Top-tier research groups or high-end startups doing foundational ML work

You should have:

Strong background in deep learning / ML research
Experience with at least one of:
Multimodal models (VLMs, vision transformers, etc.)
LLMs / generative models
Representation learning or structured prediction
A track record of building or shipping real systems, not just papers
Taste for first-principles thinking over incremental work

What Makes This Different

Greenfield research direction: You will define major parts of the roadmap
Tight feedback loop: Your work goes into production quickly
Hard, unsolved problems:
Turning perception into structured reasoning
Bridging vision + language + symbolic structure
Making AI systems reliable in the real world
Small, elite team: You’ll work directly with highly technical founders
Massive surface area: This problem sits upstream of RAG, agents, and enterprise AI

Why Join

Most frontier labs are focused on scaling general models.

We’re focused on something orthogonal and equally critical:

Making models actually work on real-world data.

If solved, this unlocks:

Reliable AI agents
Production-grade automation
Entire categories of vertical AI

This is a chance to own a foundational piece of the AI stack from day one.

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