Senior Applied Research Scientist - Core AI & Document Intelligence

Tensorlake, Inc.
San Francisco, CA, United States
24 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Application Frameworks Computer Vision Automated Storage and Retrieval Systems Encodings Information Extraction Python (Programming Language) Machine Learning Language Modeling Natural Language Processing Enterprise Software Applications
+8 more
Pytorch Retrieval-Augmented Generation Large Language Models Prompt Engineering Deep Learning Indexer Discretization Machine Learning Operations

Job description

What You’ll DoResearch & Model DevelopmentLead 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 ImpactBuild 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.QualificationsTechnical Expertise5+ 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 & EngineeringTrack record of β€œresearch 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 ApartYou 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 BenefitsWhy 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

Requirements

  • 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 β€œresearch 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 & conditions

    • Ability to save in 401(k) plans
    • Comprehensive Healthcare and Dental Benefits

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