Principal NLP Scientist
Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Jul 16, 2026.
Sourceability® is a global digital distributor of electronic components transforming how modern businesses bring products to market. With innovation, qualityandlogisticsas the backbone of the company, Sourceability’scutting-edgeproducts and services expeditethe procurement process across a wide range of industries, including communications/cellular, consumer electronics, and auto manufacturing.
The Principal NLP Scientist is a senior technical leader responsible for designing, researching, and improving advanced Natural Language Processing and Large Language Model capabilities for production business systems.
This role combines applied research, hands-on model development, technical architecture, and practical product impact. The Principal NLP Scientist will lead the design of NLP solutions for named entity recognition, text classification, text generation, semantic search, information extraction, and other language-driven automation use cases.
This is not only a research role. The focus is to take modern NLP and LLM technologies and make them reliable, measurable, maintainable, and useful inside real production workflows.
Assigned Product Group
- Product Group | NLP / AI Automation
- Stream | Software Engineering / AI & Machine Learning
- Role Type | Principal-level individual contributor / technical leader
The Principal NLP Scientist will work closely with software engineers, data engineers, product managers, analysts, and data annotation teams to define, build, evaluate, and continuously improve NLP models and language-based automation systems.
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.
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