Principal NLP Scientist

Sourceability

. Design, research, and improve advanced Natural Language Processing and Large Language Model capabilities for production business systems .

Posted 11/4/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Natural Language Processing and Large Language Models, with a strong focus on model development, evaluation, and production readiness. Proven ability to lead technical direction, collaborate across teams, and deliver measurable business value through innovative NLP solutions.

Highest-signal resume keywords

PhD In Computer Science8+ Years Of Professional Experience In Machine Learning5+ Years Of Hands-On Experience Building NLP ModelsStrong Experience With PythonStrong Experience With PyTorch And Hugging Face Transformers

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Natural Language ProcessingLarge Language ModelsNamed Entity RecognitionText ClassificationText GenerationInformation ExtractionNeural Network ArchitecturesData PreparationModel Evaluation MetricsCloud Platforms

Soft Skills

Strong Written And Verbal Communication SkillsAbility To Provide Technical Leadership

Tools & Technologies

PandasNumPySciPyScikit-learnMatplotlibSQL DatabasesMicrosoft AzureAWSDockerMLOps Practices

Industry Keywords

Agile Engineering EnvironmentsData AnnotationModel OptimizationProduction ML ObservabilityKnowledge GraphsGraph DatabasesVector DatabasesSemantic SearchRAG ArchitecturesMultilingual NLP Systems

Tech Stack

Tools & technologies

ASP.NETAWSAzureCloudDockerNeo4jNumpyPandasPythonPyTorchScikit-LearnSQL.NET

About the role

Key responsibilities & impact

Design, research, and improve advanced Natural Language Processing and Large Language Model capabilities for production business systems
Lead NLP solutions for named entity recognition, text classification, text generation, semantic search, information extraction, and language-driven automation
Define technical direction for NLP systems and evaluate new approaches
Design experiments, create prototypes, and move successful models into production
Collaborate with software engineers, data engineers, product managers, analysts, and data annotation teams
Improve automation quality, data processing accuracy, operational efficiency, and long-term AI capabilities
Review existing models, datasets, annotation processes, and production use cases
Identify high-impact NLP and LLM improvement opportunities
Define evaluation metrics and create a technical roadmap for NER, classification, generation, and information extraction
Establish standards for data labeling quality, model validation, and production readiness
Deliver prototypes or improvement proposals with measurable business value
Provide scientific and technical direction for NLP and LLM capabilities and expert guidance on technical feasibility, reliability, and production readiness

Requirements

What you’ll need

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 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: experience leading NLP or AI research initiatives in a commercial production environment
Preferred: experience with multilingual NLP systems
Preferred: experience with vector databases, embeddings, semantic search, and RAG architectures
Preferred: experience with knowledge graph concepts and graph databases such as Neo4j or FalkorDB
Preferred: experience with model serving, monitoring, drift detection, and production ML observability
Preferred: experience with Docker and containerized ML workloads
Preferred: experience with MLOps practices and CI/CD for machine learning systems
Preferred: experience working with data annotation teams and creating annotation instructions
Preferred: experience with .NET / C#, ASP.NET Core, or ML service integration into enterprise software platforms
Preferred: experience building prototypes, demos, and proof-of-concept applications
Publications, patents, or recognized technical contributions are a plus