Founding ML Researcher
About the role
Founding ML Researcher role at Unsiloed AI in San Francisco. Build and own the ML research direction and translate research into production-ready document AI models. Early-stage, proactive ownership, shaping product direction and engineering strategy.
Responsibilities
- Own the end-to-end ML lifecycle: research → experimentation → training → evaluation → production deployment
- Work with engineering team to transition research into deployed, scalable systems
- Drive best practices for data, experimentation, evaluation, and model iteration.
Qualifications
- PhD or equivalent research experience in VLM, Computer Vision or related areas (bonus)
- Publications in top-tier AI conferences (bonus)
- Familiarity with model serving, inference optimization, or deployment at scale (bonus)
- 3+ years experience in ML/AI roles (implied from Experience)
- Experience training and deploying state-of-the-art models for parsing and understanding unstructured data (preferred)
- Ability to move between theory, experimentation, and real-world deployment (required)
Skills
About Unsiloed AI
API for parsing multimodal unstructured data
AI teams spend 6+ months building document workflows, yet fewer than 10% ever reach production. Generic LLM parsers and OCR collapse on multimodal documents with text, tables, images, and charts. Poor parsing and suboptimal chunking cripple RAG pipelines and downstream automation. Unsiloed AI has built state-of-the-art vision models which serves as the infrastructure layer for turning unstructured data into structured, queryable, and LLM-ready assets. Our APIs are already parsing hundreds of thousands of documents for startups and NASDAQ-listed enterprises, powering vertical AI solutions across industries. On public benchmarks, Unsiloed AI consistently outperforms solutions from LlamaIndex, Gemini, Mistral, and Unstructured.io among others.
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