Principal Engineer, NLP
About the role
- 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
- SummarizationQuestion answering
- Conversational AIKnowledge 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 ModelsDesign 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.
- 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 ScaleBuild 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 AIRetrieval systems
- Representation learningDeep 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 InTransformer architectures
- Large Language Models
- Retrieval-Augmented Generation (RAG)
- Semantic search
- Information retrieval
- Knowledge extraction
- Entity recognition
- Relation extraction
- Text understanding
- Conversational AIMachine learningDeep learning
- Representation learning
- Graph-enhanced NLPVector search systemsKnowledge graphs
- Strong Programming SkillsPythonPyTorchTensorFlow/JAXSparkDistributed 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 infrastructureAI observability and evaluation platforms
- 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, WWW, 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.
- Enterprise search
- Enterprise copilots
- Document intelligence
- Retrieval systemsKnowledge extractionAI 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.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
