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About This Role
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, 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.
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 committed to actively working to foster an inclusive environment that celebrates people for all of who they are
#LI-CP2
US Pay Rate: $182,700.00 - $228,400.00 - $274,000.00 Annually
Starting pay for the successful applicant will depend on geographic location, internal equity, job-related knowledge, skills, and candidate experience. Sales roles will be eligible for commission payments tied to quota achievement. All other permanent roles will be eligible for one of our annual incentive plans, which are based on company financial attainment and individual performance. Employees in this position are eligible to participate in the Company’s benefits programs, which generally include health care, life and disability insurance, retirement savings, and time-off programs. Benefit details vary by country and plan, including eligibility and available options, and will be shared during the hiring process. Additional information is available here: https://www.teradata.com/About-Us/Careers/Benefits Employees in this position are also eligible to participate in the Company’s comprehensive benefits programs, which include healthcare, life and disability insurance plans, a 401(k)-retirement savings plan, and time-off programs. Specific details of these benefits, including eligibility criteria and plan options, will be provided during the hiring process and can be reviewed here: https://www.teradata.com/About-Us/Careers/Benefits Teradata is proud to be an equal opportunity employer. We do not discriminate based upon race, color, ancestry, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related conditions), national origin, sexual orientation, age, citizenship, marital status, disability, medical condition, genetic information, gender identity or expression, military and veteran status, or any other legally protected status. We welcome and encourage individuals from all backgrounds to apply and join our team, bringing their unique perspectives and experiences to help us innovate and grow. If you require accommodations during the interview process, please let your recruiter know and we will work with you to meet your needs.
Salary Context
This $182K-$274K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Teradata, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($228K) sits 8% above the category median. Disclosed range: $182K to $274K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Teradata AI Hiring
Teradata has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Diego, CA, San Diego, CA, US, London, England, UK. Compensation range: $274K - $274K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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