Applied Research Scientist - Core AI & Document Intelligence

• 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., * 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.

• 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., * 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.