Applied Scientist, Search

About the Role

As an Applied Scientist at [Employer hidden — sign up to reveal], you will:

• Innovate & Deliver: Design, build, test, and deploy end-to-end AI solutions for complex document understanding tasks in the legal domain.
• Develop advanced models for semantic chunking of lengthy, non-uniformly structured legal documents with adjustable granularity levels for different use cases.
• Build document enrichment systems that classify documents according to legal and customer-defined taxonomies and extract rich metadata.
• Create LLM-based knowledge graph construction pipelines that extract and link heterogeneous legal knowledge including citations, entities, and legal concepts across diverse legal content.
• Develop scalable synthetic data generation systems to support model training, simulate complex legal research queries and generate hallucination-free answers.
• Work in collaboration with engineering to ensure well-managed software delivery and reliability at scale.
• Evaluate & Optimize: Develop comprehensive data and evaluation strategies for both component-level and end-to-end quality, leveraging expert human annotation and synthetic data generation.
• Apply robust training and evaluation methodologies that balance model performance with latency requirements, particularly for SLM-based solutions. You'll apply knowledge distillation techniques to compress large models into efficient SLMs suitable for production deployment.
• Drive Technical Decisions: Independently determine appropriate architectures for challenging document understanding problems including: semantic chunking strategies that handle diverse document formats, preserve legal document structure, and adapt to different granularity needs; document classification approaches that work across varying legal taxonomies and generalize to customer-defined schemas; LLM-based knowledge extraction methods that handle challenges like citation recognition errors and contextual references; multi-document reasoning architectures for generating synthetic multi-hop queries that reflect complex legal research patterns.
• Balance accuracy, efficiency, and scalability while solving real-world challenges like handling diverse document formats and content types.
• Align & Communicate: Partner closely with Engineering and Product teams to translate complex legal document understanding challenges into scalable, production-ready solutions.
• Engage stakeholders across multiple product lines to deeply understand use case requirements, shaping objectives that align document understanding capabilities with diverse business needs including next-generation search and deep legal research.
• Advance the Field: Maintain scientific and technical expertise in one or more relevant areas as demonstrated through product deliverables, published research at top venues (e.g., ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD) , and intellectual property.

About You

You're a fit for the role of Applied Scientist if you have:

• PhD in Computer Science, AI, NLP, or a related field, or a Master's with equivalent research/industry experience
• Demonstrable hands-on experience building and deploying document understanding systems, information extraction pipelines and knowledge distillation, or knowledge graph construction using deep learning, LLMs and NLP methods.
• Solid understanding of synthetic data generation techniques for NLP, including query - answer generation with verification and scalable data augmentation for training specialized models
• Deep understanding of document understanding fundamentals: document layout analysis, semantic chunking approaches beyond fixed-size or paragraph-based methods, document classification handling hierarchical taxonomies, imbalanced multi-label classification, and adapting to domain-specific schemas.
• Proven ability to translate complex document understanding problems into innovative AI applications that balance accuracy and efficiency
• Professional experience scaling yourself and leading through others, in an applied research setting
• Strong programming skills (e.g., Python) and experience with modern deep learning frameworks (e.g., PyTorch, Hugging Face Transformers, DeepSpeed)
• Publications at relevant venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD
• Solid understanding of DL/ML approaches used for NLP tasks
• Experience designing annotation workflows, creating high-quality labeled datasets with clear guidelines, and developing evaluation frameworks for document understanding tasks.

What’s in it For You?

• Hybrid Work Model: Flexible hybrid working environment (2-3 days a week in the office depending on the role)
• Flexibility & Work-Life Balance: Flex My Way policies including work from anywhere for up to 8 weeks per year
• Career Development and Growth: Grow My Way programming and skills-first approach
• Industry Competitive Benefits: Comprehensive benefit plans including flexible vacation, mental health days, retirement savings, tuition reimbursement, etc.
• Culture: Globally recognized, award-winning reputation for inclusion and belonging
• Social Impact: Two paid volunteer days off annually and pro-bono consulting projects
• Making a Real-World Impact: Helping customers pursue justice, truth, and transparency