Product Group Focus Areas
The NLP product group is responsible for building and improving systems related to:
Named entity recognition and structured data extraction
Text classification and categorization
Text generation and language-based automation
Large Language Model evaluation, adaptation, and integration
Retrieval-augmented generation and semantic search
Knowledge graph and GraphRAG-based approaches for connecting structured business data, unstructured text, and entity relationships in AI assistant workflows
Data preparation, annotation strategy, and labeling quality
Model evaluation, monitoring, and production performance
Applied NLP research and prototype development
Integration of NLP models into internal business applications
Insight on Your Impact
In this role, you will influence how the company uses modern NLP and LLM technologies across internal platforms and operational workflows.
You will define technical direction for NLP systems, evaluate new approaches, design experiments, create prototypes, and help move successful models into production. Your work will directly affect automation quality, data processing accuracy, operational efficiency, and the long-term AI capabilities of the company.
The role requires strong scientific depth, but also practical engineering judgment. The right candidate should be able to read research papers, understand model architecture, design measurable experiments, and also work with engineers to make sure the final solution can run reliably in production.
Your Qualifications, Your Influence
To be successful in this role, you should have
PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Linguistics, Applied Mathematics, Data Science, or a closely related technical field
8+ years of professional experience in machine learning, artificial intelligence, or NLP
5+ years of hands-on experience building NLP models for production or near-production systems
Deep understanding of modern neural network architectures, including RNN, CNN, Transformer-based architectures, attention mechanisms, embeddings, fine-tuning strategies, layers, modules, and loss functions
Strong practical experience with NLP tasks such as NER, classification, text generation, semantic similarity, information extraction, and document understanding
Strong experience with Large Language Models, including model evaluation, prompt design, fine-tuning, retrieval-augmented generation, and safe production usage
Practical understanding of RAG, GraphRAG, knowledge graphs, embeddings, and hybrid retrieval approaches for production LLM applications
Strong hands-on experience with Python
Strong experience with PyTorch and Hugging Face Transformers
Experience with ONNX or other model optimization / model serving formats
Strong understanding of data preparation, data quality, labeling workflows, annotation guidelines, and model evaluation metrics
Practical experience with main data analysis and machine learning libraries, including Pandas, NumPy, SciPy, scikit-learn, and Matplotlib
Experience working with SQL databases and structured business data
Experience with cloud platforms such as Microsoft Azure or AWS
Ability to design experiments, define success metrics, compare model approaches, and explain trade-offs clearly
Strong written and verbal English communication skills
Experience working in Agile engineering environments
Ability to provide technical leadership without requiring formal people management authority
Preferred Skills and Technical Familiarity
The following experience will be helpful
Experience leading NLP or AI research initiatives in a commercial production environment
Experience with multilingual NLP systems
Experience with vector databases, embeddings, semantic search, and RAG architectures
Experience with knowledge graph concepts, including entity and relationship modeling, graph schema design, traversal queries, and LLM integration with graph databases such as Neo4j, FalkorDB, or similar technologies
Experience with model serving, monitoring, drift detection, and production ML observability
Experience with Docker and containerized ML workloads
Experience with MLOps practices and CI/CD for machine learning systems
Experience working with data annotation teams and creating annotation instructions
Experience with .NET / C#, ASP.NET Core, or integration of ML services into enterprise software platforms
Experience building prototypes, demos, and proof-of-concept applications for new AI capabilities
Publications, patents, or recognized technical contributions in NLP, machine learning, or applied AI are a plus
Success in the First 90 Days
During the first 90 days, the Principal NLP Scientist is expected to
Understand the current NLP and AI automation landscape inside the company
Review existing models, datasets, annotation processes, and production use cases
Identify the highest-impact opportunities for NLP and LLM improvements
Define practical evaluation metrics for current and future NLP models
Create a technical roadmap for improving NER, classification, generation, and information extraction capabilities
Propose clear standards for data labeling quality, model validation, and production readiness
Deliver at least one meaningful prototype or improvement proposal with measurable business value
Establish strong working relationships with engineering, product, data, and operations stakeholders
What This Role Does Not Own
This role does not own general IT infrastructure, end-user support, business operations, or manual data entry processes.
The Principal NLP Scientist is also not the sole owner of product priorities or business requirements. Product management owns business prioritization, backlog structure, and stakeholder alignment. This role owns the scientific and technical direction for NLP and LLM capabilities and provides expert guidance on what is technically possible, reliable, and production-ready.
EQUAL OPPORTUNITY EMPLOYER.
It is our policy to abide by all federal, state and local laws prohibiting employment discrimination based on a person’s race, color, religious creed, sex, national origin, ancestry, citizenship status, pregnancy, childbirth, physical disability, mental and/or intellectual disability, age, military status, veteran status (including protected veterans), marital status, registered domestic partner or civil union status, familial status, gender (including sex stereotyping and gender identity or expression), medical condition (including, but not limited to, cancer related or HIV/AIDS related), genetic information, sexual orientation, or any other protected status.
