The landscape of AI is evolving rapidly, and PandaDoc 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.
What makes this role unique?
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Document Intelligence at Scale: Your work will directly power PandaDoc’s core AI capabilities—from layout detection and OCR to structured extraction, retrieval, and document-based reasoning.
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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.
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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.
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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.
In this role, you will:
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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.
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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.
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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.