Lead research and product development of document understanding and multimodal AI. Design and evaluate VLMs, RAG and retrieval systems; build benchmarks and datasets; ship production-ready components (table extraction, structure parsing, semantic indexing, multimodal QA); and work with engineering, product, and customers to deploy models and agentic workflows at scale.
About Tensorlake Inc
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.
- 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.
Qualifications
Technical Expertise- 5+ years experience in AI/ML research and applied systems; strong record of delivering results.
- Deep background in document understanding, multimodal AI, NLP + computer vision integration.
- Hands-on experience with vision-language models, RAG frameworks, context-aware retrieval, agentic AI, and embedding-based systems.
- Track record of “research → product” delivery: turning prototypes into robust pipelines, APIs, or services.
- Experience optimizing and fine-tuning large models, knowledge of quantization/LoRA/efficient training.
- Proficiency with deep learning frameworks (PyTorch preferred), Python, and scalable ML tooling.
- Experience with open-source frameworks and community contributions
- Background in agents, RAG architectures, retrieval systems, and context engineering.
- Experience designing benchmarks, quality metrics, or curated datasets for complex tasks.
What Sets You Apart
- 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.
Benefits
- - Ability to save in 401(k) plans
- - Comprehensive Healthcare and Dental Benefits
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
Top Skills
Python,Pytorch,Vision-Language Models,Rag,Retrieval Frameworks,Embeddings,Lora,Quantization,Llm Orchestration,Agentic Ai,Serverless,Apis,Scalable Ml Tooling
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