Senior Applied Research Scientist – Core AI & Document Intelligence
At a glance
Senior AI Research role at Tensorlake. San Francisco · full-time · no salary stated · we estimate $226,375–$297,000.
Tensorlake has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Lead experiments on models that interpret documents, combine modalities and extract deeper content
- Assess and integrate vision-language models, retrieval and RAG methods, and grounding approaches
- Create benchmarks, datasets and evaluation methods for practical document AI tasks
- Build production AI features such as table extraction, document parsing, semantic indexing and multimodal question answering
- Work with engineering, product, customers and partners to deploy models and validate use cases
What you bring
- At least five years in AI/ML research and applied systems, with a record of delivering results
- Strong grounding in document understanding, multimodal AI, and combining NLP with computer vision
- Hands-on work with vision-language models, RAG, context-aware retrieval, agentic AI and embedding-based systems
- Experience taking research prototypes into dependable pipelines, APIs or services
Who this fits
This suits a senior individual contributor who can connect ML research with engineering delivery and has shipped systems for developers or customers. The role is based in San Francisco.
From the employer
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