Principal NLP Scientist

Sourceability

About The Position

Sourceability® is a global digital distributor of electronic components transforming how modern businesses bring products to market. With innovation, quality and logistics as the backbone of the company, Sourceability’s cutting-edge products and services expedite the 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.

Requirements

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

Nice To Haves

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

Responsibilities

Designing, researching, and improving advanced Natural Language Processing and Large Language Model capabilities for production business systems.
Leading the design of NLP solutions for named entity recognition, text classification, text generation, semantic search, information extraction, and other language-driven automation use cases.
Taking modern NLP and LLM technologies and making them reliable, measurable, maintainable, and useful inside real production workflows.
Working 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.
Influencing how the company uses modern NLP and LLM technologies across internal platforms and operational workflows.
Defining technical direction for NLP systems, evaluating new approaches, designing experiments, creating prototypes, and helping move successful models into production.
Understanding the current NLP and AI automation landscape inside the company.
Reviewing existing models, datasets, annotation processes, and production use cases.
Identifying the highest-impact opportunities for NLP and LLM improvements.
Defining practical evaluation metrics for current and future NLP models.
Creating a technical roadmap for improving NER, classification, generation, and information extraction capabilities.
Proposing clear standards for data labeling quality, model validation, and production readiness.
Delivering at least one meaningful prototype or improvement proposal with measurable business value.
Establishing strong working relationships with engineering, product, data, and operations stakeholders.