Lead Applied Scientist, Document Understanding

What you'll do

• Build and deploy semantic chunking, document enrichment, and knowledge-graph systems for complex documents.
• Lead model distillation, synthetic-data generation, and end-to-end evaluation; guide architecture and technical decisions.
• Partner with engineering and product teams, advise senior leadership on AI strategy, and mentor applied scientists.

What they ask for

• PhD in Computer Science, Artificial Intelligence, Natural Language Processing, or a related field.
• 8+ years of industry experience building and scaling document-understanding, information-extraction, knowledge-graph, or applied NLP solutions.
• End-to-end ML lifecycle expertise, including model development, evaluation, optimization, distillation, and production deployment.
• Strong Python skills and hands-on PyTorch, Hugging Face Transformers, and DeepSpeed experience; research publication record at leading conferences or equivalent.

Nice to have

• Legal document understanding, legal IE, or legal AI experience; complex document structures.
• Retrieval or QA over large document collections, RAG and agentic workflows, knowledge-graph frameworks, AzureML, or AWS SageMaker.

Pay

$147,600 USD - $274,200 USD (base salary); $140,000 CAD - $175,000 CAD (base salary)

Tools

Python, PyTorch, Hugging Face Transformers, DeepSpeed, AzureML, AWS SageMaker

AI focus

Applied AI for document understanding, including semantic chunking, enrichment, LLM-based knowledge-graph construction and information extraction, model distillation into production SLMs, synthetic data, and evaluation.