Lead Applied Scientist, Document Understanding
United States of America, New York, New York
R
USD 140K-274K Senior-level Full Time
Tasks
- Build document enrichment systems using taxonomies
- Design and deploy semantic chunking models for legal documents
- Design component and end to end evaluation frameworks
- Develop LLM based knowledge graph construction pipelines
- Extract and link citations entities and legal concepts
- Generate synthetic data for evaluation
- Lead knowledge distillation to compress large models into SLMs
- Mentor applied scientists and ML practitioners
- Own architecture chunking and knowledge extraction decisions
- Partner with engineering for reliability and scale
- Provide technical input to AI strategy and roadmap
Perks/Benefits
- Employee assistance program
- Employee stock purchase plan
- Fitness reimbursement
- Flexible work arrangements
- Headspace app access
- Mental health days
- Paid volunteer days
- Retirement savings plan
- Tuition reimbursement
- Work from anywhere up to 8 weeks per year
Skills/Tech-stack
AWS SageMaker | AzureML | Citation Parsing | Data Generation | DeepSpeed | Document Layout Analysis | Document enrichment | Document layout | Entity Linking | Entity recognition | Evaluation Frameworks | Few-Shot Learning | Few-shot | Hugging Face | Hugging Face Transformers | Information Extraction | Knowledge Distillation | Knowledge graphs | LLM | Language Processing | Layout analysis | Model Compression | Multi-task Learning | Natural Language | Natural Language Processing | PyTorch | Python | RAG | Relation extraction | Retrieval-Augmented Generation | Semantic chunking | Synthetic Data Generation | Synthetic data | Taxonomies
Education
Roles
Applied Scientist | Lead | Lead Applied Scientist | Scientist
Regions
Countries
States
Cities
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