Lead AI Researcher

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 RoleWe’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 modelsVision-language reasoningStructured generation / parsingReliability and determinism in AI systemsYou’ll have end-to-end ownership—from first-principles research → model design → training → production deployment.What You’ll Work OnDesigning novel architectures for multimodal understanding (documents, tables, layouts, graphs)Pushing beyond standard LLM paradigms into structure-aware and layout-aware modelsImproving factuality, determinism, and reliability in model outputsBuilding systems that combine:Vision modelsLanguage modelsStructured decoding / constrained generationDeveloping evaluation frameworks for real-world correctness (not just benchmark scores)Shipping research directly into production systems used by real customersWho This Is ForWe’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 workYou should have:Strong background in deep learning / ML researchExperience with at least one of:Multimodal models (VLMs, vision transformers, etc.)LLMs / generative modelsRepresentation learning or structured predictionA track record of building or shipping real systems, not just papersTaste for first-principles thinking over incremental workWhat Makes This DifferentGreenfield research direction: You will define major parts of the roadmapTight feedback loop: Your work goes into production quicklyHard, unsolved problems:Turning perception into structured reasoningBridging vision + language + symbolic structureMaking AI systems reliable in the real worldSmall, elite team: You’ll work directly with highly technical foundersMassive surface area: This problem sits upstream of RAG, agents, and enterprise AIWhy JoinMost 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 agentsProduction-grade automationEntire categories of vertical AIThis is a chance to own a foundational piece of the AI stack from day one.