Senior Applied Research Scientist – Core AI & Document Intelligence

**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**.

**What You’ll Do**

**Research & Model Development**

• 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.

**Applied AI & Product Impact**

• 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.

**Applied AI & Engineering**

• 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.

**Optional (but Highly Valued)**

• 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

**Why Tensorlake?**

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](https://www.linkedin.com/company/tensorlake/)