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Tensorlake Inc in San Francisco is seeking an experienced AI/ML researcher to lead design and experimentation on state-of-the-art models for document understanding and multimodal reasoning. You will evaluate vision-language models, RAG frameworks, and grounding techniques to drive product impact, and develop benchmarks and evaluation methods for real-world tasks.
You will build production-ready AI components (table extraction, structure parsing, semantic indexing) and collaborate with
Lead design and experimentation on state-of-the-art models for document understanding, multimodal reasoning, and deep content extraction.
Research, evaluate, and integrate the latest vision-language models (VLMs), retrieval frameworks, RAG systems, and grounding techniques to drive product impact.
Develop new benchmarks, datasets, and evaluation methodologies tailored to real-world document AI tasks.
Build and ship production-ready AI components (e.g., table extraction, structure parsing, semantic indexing, multimodal QA, agentic workflows).
Work with engineering and product partners to deploy models at scale - from prototype to integrated platform features.
Collaborate closely with customers and partners to prioritize and validate use cases, including LLM orchestration, context engineering, and agent integration.
You balance deep technical curiosity with product focus and can speak fluently to both ML research and engineering issues.
You've shipped models and systems that are used by developers or customers in real scenarios (not just research demos).
You are comfortable working in a fast-moving startup environment where priorities evolve and innovation is part of the culture.
We're building something foundational for how companies integrate AI into knowledge workflows, RAG pipelines, document agents, and real-world enterprise applications . If you're excited about shaping AI products that matter, and you enjoy bridging research with engineering execution, this is the role to join. LinkedIn
Tensorlake is a serverless platform for agentic applications and Document Ingestion. It provides foundational infrastructure such as serverless compute, code sandboxes, durable execution and unstructured data ingestion APIs to developers in enterprises. We aim to push the frontier of AI that understands real, complex documents and detailed workflows - not just text extraction, but contextual reasoning and scalable application systems .
This role is for an experienced scientist who thrives both in innovating foundational models and building usable, robust AI systems for real users .