ML Engineer - Document Intelligence & Applied GenAI

The landscape of AI is evolving rapidly, and Panda Doc is investing heavily in machine learning to power the next generation of intelligent document workflows. Our goal is to build scalable, production-grade AI systems that automate document understanding, extract structured data at scale, and enable new AI-first product experiences for tens of thousands of businesses.

As an ML Engineer focused on Document Intelligence and GenAI, you will design, train, evaluate, and optimize models that transform unstructured documents into high-quality structured data. You’ll work across the full stack of model development—datasets, training, inference, deployment pipelines—and help bring cutting-edge research into real production systems at scale.

• Document Intelligence at Scale: Your work will directly power Panda Doc’s core AI capabilities—from layout detection and OCR to structured extraction, retrieval, and document-based reasoning.
• High Ownership, High Impact: You will design end-to-end ML systems, influence roadmap decisions, and work closely with product, engineering, and design to define requirements and ship production AI features.
• Real-World ML Challenges: You’ll tackle model robustness, evaluation, latency, observability, RAG quality, model routing, and the complexities of deploying AI systems that must perform reliably on millions of documents.
• Deep GenAI Integration: You’ll experiment with frontier and open-source models, integrate vision–language systems, and build efficient pipelines for inference, guardrails, fine-tuning, and document-aware reasoning.

• Model Development & Evaluation
  • Build and maintain evaluation frameworks for document models, LLMs, OCR, and structured extraction.
  • Define metrics, benchmarks, and validation strategies for real-world document workloads.
• Dataset & Pipeline Creation
  • Design and curate high-quality datasets for supervised training, fine-tuning, and validation.
  • Create scalable preprocessing pipelines for PDFs, scans, images, forms, and semi-structured documents.
• Model Training & Fine-Tuning
  • Train and fine-tune transformer-based OCR, VLMs, layout models, and open-source LLMs for document understanding tasks.
  • Optimize models for reliability, accuracy, and cost efficiency in production environments.
• Inference & Deployment
  • Deploy ML models with modern inference runtimes (vLLM, TGI, Tensor
    
    RT, ONNX Runtime).
  • Build guardrails, monitoring, and fallback mechanisms to ensure safe and predictable model behavior.
• RAG & Document Reasoning
  • Develop retrieval and chunking strategies tailored to document structures (tables, forms, multi-page PDFs).
  • Optimize end-to-end RAG pipelines for semantic search, Q&A, and workflow automation.
• Cross-Functional Collaboration
  • Partner with PMs, backend engineers, and product designers to define AI opportunities and translate requirements into technical solutions.

We are expanding our AI/ML function with an ML Engineer who specializes in document intelligence
, vision–language models
, and LLM-based extraction and reasoning
. You should be comfortable with both traditional document AI approaches and cutting-edge GenAI workflows. You thrive in fast-moving environments, are self-directed, and enjoy solving practical ML problems that directly impact customers.

• Vision transformers, layout models, and OCR systems
• Structured extraction from complex documents
• RAG for document-heavy workloads
• Optimizing LLM pipelines for cost, accuracy, and throughput
• Deploying and benchmarking models in real production systems

• 5+ years of Python experience
• Experience training, fine-tuning, and deploying traditional computer vision models for document intelligence tasks (layout detection, table extraction, OCR, information extraction)
• Hands‑on experience with document understanding frameworks and models:
  • Traditional document AI models (Layout
    
    LM, Donut, Doc Former)
  • Modern vision‑language models with OCR capabilities (Deep Seek‑OCR, Light
    
    OnOCR‑1B, etc.)
  • Experience deploying and optimizing models using inference frameworks such as vLLM (preferred), TGI, Tensor
    
    RT, or ONNX Runtime
  • Experien…