Interested in this AI/ML Engineer role at Teradata?
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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
As Head of Product Operations \& PMO, you will build and lead the operating system for Teradata's global Product organization — driving the rhythm, rigor, and cross\-functional alignment required to deliver world\-class data and analytics products at scale. You will bring experience in cutting\-edge product delivery methods along with an eye for process re\-engineering and use of relevant AI technologies to accelerate product delivery while maintaining high quality, cost, and customer value
- Define and own the Product org's operating model, including planning cycles, roadmap governance, portfolio reviews, and OKR/metric frameworks.
- Lead and develop a high\-performing PMO function covering program delivery, risk management, dependency tracking, and executive\-level reporting.
- Partner with the CPO, product leadership, engineering, and go\-to\-market teams to ensure strategic initiatives are resourced, sequenced, and delivered on time.
- Drive operational excellence across the product lifecycle — from intake and prioritization through launch readiness and post\-launch learning.
- Establish standards for product documentation, cross\-team communication, and decision governance that reduce friction and increase velocity.
- Own the Product leadership cadence: QBRs, monthly business reviews, leadership offsites, and board\-level reporting artifacts.
- Identify systemic bottlenecks across Product, Engineering, and GTM and design scalable solutions that improve speed, predictability, and quality.
- Apply process automation and AI strategies to bring transformational change to productivity, innovation, and impact across our workforce.
Who You’ll Work With
You will serve as a strategic force multiplier for Teradata’s Product organization as well as other corporate functions who play a role in product delivery including Operations, GTM, Legal, and Finance. You will work daily with:
- VP of Product Operations, Partnerships, and PMO and VP\-level product leaders across platform, analytics, cloud, and AI product lines.
- Product Management, Engineering, Design, Operations, Marketing, Sales, Finance, and Legal stakeholders to align plans and remove blockers.
- Teradata's executive team (ELT) on portfolio visibility, investment decisions, and enterprise\-wide transformation programs.
- Talent Acquisition, HR, and Finance partners on headcount planning, budget management, and organizational design.
- Colleagues who share a commitment to leveraging AI responsibly, ensuring our people and customers benefit from the opportunities AI creates.
What Makes You a Qualified Candidate
- 12\+ years of experience in product management and/or product operations roles plus program management skills within enterprise software or cloud technology companies.
- 5\+ years of direct people management experience with proven ability to cultivate highly motivated, high performance teams.
- Proven track record building and scaling PMO or product operations functions in large organizations, utilizing technology including automation and/or AI where appropriate, to achieve step\-function business outcomes.
- Deep experience leading complex, cross\-functional programs involving cloud platforms, data infrastructure, or analytics products.
- Exceptional executive communication skills — able to synthesize complex information into crisp, decision\-ready narratives for C\-suite and board audiences.
- Demonstrated ability to influence without authority and drive alignment across engineering, product, and commercial organizations.
- Mastery of foundational AI skills and the ability to understand how AI can be applied to improve business outcomes. Passion to continue AI learning, and hunger to roll up sleeves and apply them to achieve tangible efficiencies.
What You’ll Bring
- Experience with Agile, SAFe, or hybrid delivery methodologies at enterprise scale.
- Familiarity with Teradata Vantage, cloud data warehousing, or open table format ecosystems (Apache Iceberg, Delta Lake) is a plus.
- Strong analytical skills — comfortable defining KPIs, building dashboards, and using data to identify organizational health trends.
- Background in enterprise SaaS, cloud infrastructure, or data analytics platforms strongly preferred.
- Experience with OKR frameworks and product portfolio management tools (e.g., Productboard, Aha!, Jira Align).
- An operator's mindset: you close loops, follow through, and hold the system accountable.
- A passion for how AI can unlock potential to help our teams, our customers, and our communities achieve great things.
Why We Think You Will 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 an anti\-racist company because our dedication to Diversity, Equity, and Inclusion is more than a statement. It is a deep commitment to doing the work to foster an equitable environment that celebrates people for all of who they are.
\#LI\-CP2
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Teradata AI Hiring
Teradata has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span CA, US, US. Compensation range: $325K - $325K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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