Principal Engineer, NLP

Our company

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

What You’ll Do

We are building the next generation of AI-powered knowledge, reasoning, and autonomous agent platforms that operate at enterprise scale. We are seeking an exceptional Principal NLP Engineer to own and lead the design, development, and deployment of advanced natural language technologies powering semantic and context graphs, context analysis, intelligent search, retrieval, question answering, knowledge extraction, reasoning systems, AI agents, and enterprise copilots for Teradata Context Engine, our Autonomous Knowledge Platform. Context Engine is a platform that converts enterprise data estates into governed, agent-ready knowledge. Built for organizations where AI reliability and auditability are non-negotiable, Context Engine enables companies to deploy AI workflows on the data they already own — without rebuilding their infrastructure, without retraining their models, and without waiting for generic AI synthesis to learn the business terminology that took their analysts years to master.

This is a deeply hands‑on technical leadership role for a recognized expert in Natural Language Processing who combines strong research foundations with proven experience shipping production systems that serve millions of users and process billions of documents. You will work at the intersection of large language models, information retrieval, knowledge graphs, semantic understanding, and agentic AI.

The ideal candidate has a track record of driving breakthrough NLP capabilities from research to production and possesses deep expertise across classical NLP, modern transformer architectures, retrieval‑augmented generation (RAG), multimodal understanding, information extraction, and large‑scale language model systems.

Technical Leadership

• Define and execute the strategic roadmap for NLP and language understanding capabilities across our Knowledge Platform.
• Serve as the technical authority for large‑scale NLP architecture, algorithms, and production deployment.
• Drive innovation in enterprise language understanding, semantic search, retrieval systems, knowledge extraction, and AI reasoning.

NLP and Language Intelligence

• Design and build advanced NLP systems for:
  • Entity extraction
  • Relationship extraction
  • Information extraction
  • Document understanding
  • Semantic indexing
  • Context understanding and representation
  • Text classification
  • Topic modeling
  • Summarization
  • Question answering
  • Conversational AI
  • Knowledge grounding
  • Multi‑document reasoning
  • Extracting context from multi‑modal sources of language
  • Multi‑lingual solutions
  • Machine translation
• Develop systems that transform unstructured enterprise content into structured knowledge assets.
• Advance techniques for long‑context understanding, document intelligence, and multi‑hop reasoning.
• Build domain‑adaptive language understanding models across diverse enterprise data sources.
• Develop solutions with highest quality and performance
• Develop solutions that are tested and validated

Large Language Models

• Design and optimize applications built on frontier foundation models.
• Build advanced retrieval‑augmented generation architectures for enterprise‑scale environments.
• Develop agentic workflows that leverage LLMs for planning, reasoning, execution, and tool usage.
• Create evaluation methodologies that measure factuality, grounding, trustworthiness, and task completion.
• Improve model performance through prompt engineering, fine‑tuning, continued pretraining, synthetic data generation, and reinforcement‑learning‑based approaches.

Knowledge and Semantic Systems

• Design NLP pipelines that power enterprise knowledge graphs and semantic layers.
• Develop ontology‑aware language understanding systems.
• Build entity resolution, canonicalization, taxonomy mapping, and concept extraction capabilities.
• Connect language understanding with graph‑based reasoning and enterprise knowledge discovery platforms.

Platform and Scale

• Build highly scalable distributed NLP systems processing petabytes of enterprise content.
• Optimize inference architectures for latency, throughput, cost efficiency, and reliability.
• Lead deployment strategies across on‑prem, cloud‑native, hybrid, and enterprise environments.
• Partner closely with platform engineering teams to operationalize NLP services at global scale.

Research and Innovation

• Stay at the forefront of advances in:
  • Foundation models
  • Agentic AI
  • Retrieval systems
  • Representation learning
  • Deep learning
  • Knowledge‑enhanced language models
  • Reasoning systems
• Publish, patent, contribute to open source, and represent the organization externally at premier AI conferences and industry events.
• Mentor senior engineers and help establish NLP engineering best practices across the company.

Who You’ll Work With

Partner with distinguished engineers, architects, researchers, and product leaders to shape the future of Teradata enterprise AI.

What Makes You a Qualified Candidate

• PhD or MS in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, NLP, or related field.
• 12+ years of software engineering experience, including 8+ years building production NLP and machine learning systems.
• Recognized industry leader in Natural Language Processing, Language AI, Search, Retrieval, or LLM‑based systems.
• Deep hands‑on expertise developing and deploying large‑scale NLP systems in production.

Deep Expertise In

• Transformer architectures
• Large Language Models
• Retrieval‑Augmented Generation (RAG)
• Semantic search
• Information retrieval
• Knowledge extraction
• Entity recognition
• Relation extraction
• Text understanding
• Conversational AI
• Machine learning
• Deep learning
• Representation learning
• Graph‑enhanced NLP
• Vector search systems
• Knowledge graphs

Strong Programming Skills

• Python
• PyTorch
• TensorFlow/JAX
• Spark
• Distributed computing frameworks

Experience With

• Hugging Face ecosystem
• Fine‑tuning and adaptation frameworks
• Large‑scale model evaluation
• Vector databases
• Embedding models
• Search engines
• Knowledge graphs
• Distributed ML infrastructure
• AI observability and evaluation platforms

Preferred Qualifications

• PhD with published research in NLP, IR, ML, or AI in top‑tier peer reviewed journals and conferences
• Experience building systems that serve hundreds of millions of users or process billions of documents.
• Contributions to major NLP open‑source ecosystems.
• Publications in ACL, EMNLP, NAACL, COLING, NeurIPS, ICML, ICLR, KDD, SIGIR, or related venues.
• Experience leading architecture for enterprise copilots, intelligent assistants, or autonomous agents.
• Expertise in enterprise search and knowledge management systems.
• Experience with graph‑based reasoning and knowledge graph technologies.
• Patent portfolio in NLP, machine learning, search, or language understanding.

What You’ll Bring

• Established reputation as an industry expert in NLP and language AI.
• Proven record of taking breakthrough research into production at scale.
• Experience building state‑of‑the‑art systems involving:
  • Enterprise search
  • Enterprise copilots
  • Document intelligence
  • Retrieval systems
  • Knowledge extraction
  • AI agents
  • Semantic reasoning
  • Knowledge graphs
• Ability to influence technical strategy across multiple organizations.
• Strong balance of scientific rigor, engineering excellence, and product impact.

Success Metrics

Within 12 months, you will:

• Define next‑generation NLP and language intelligence architecture for the platform.
• Deliver measurable improvements in retrieval quality, answer accuracy, grounding, and user satisfaction.
• Establish world‑class evaluation and benchmarking frameworks.
• Lead development of enterprise‑scale knowledge extraction and semantic understanding capabilities.
• Enable agentic AI experiences that leverage enterprise knowledge with high trust, transparency, and precision.
• Raise the organization’s NLP and AI capabilities to industry‑leading levels.

Why We Think You'll Love Teradata

We prioritize a people‑first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well‑being because we care about our people and their ability to thrive both personally and professionally. We are committedto actively workingto foster an inclusive environment that celebrates peoplefor all of who they are

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