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Position Summary...
What you'll do...
About the Role
Advertising is entering a fundamental transition.
As consumers increasingly discover products, make decisions, and complete tasks through AI-powered experiences and agents, the traditional constructs of search results, sponsored listings, display placements, and even advertising interfaces themselves will evolve.
We are looking for a Group Director of Product Management to help define what comes next.
This leader will own the product strategy and execution for Agentic Advertising, shaping how brands participate in AI-powered commerce experiences across Walmart and the broader ecosystem. The role spans advertising experiences within Walmart’s own agentic surfaces, including experiences such as Sparky, as well as emerging partner platforms, agent-to-agent interactions, and entirely new advertising products and business models.
This is not a role focused on simply inserting existing ad formats into new surfaces. The mandate is to rethink advertising from first principles for a world in which consumers increasingly delegate discovery, research, comparison, and purchasing decisions to intelligent agents.
The right leader will be deeply curious about AI, agents, advertising, and commerce; comfortable operating ahead of established playbooks; and energized by turning emerging technology into scalable customer experiences and meaningful new businesses.
What You’ll Do
Define the future of advertising in an agentic world
Develop the long-term product vision and strategy for Agentic Advertising.
Start from first principles: as consumers increasingly interact with agents rather than traditional interfaces, determine where advertising creates genuine value for customers, advertisers, partners, and Walmart.
You will explore questions such as:
- What does a relevant, useful commercial experience look like when an AI agent is helping a customer plan, discover, compare, or buy?
- How should brands participate in conversational and agentic experiences without undermining customer trust?
- What advertising constructs emerge when the primary interface is an agent rather than a page, feed, or search results screen?
- How should paid and organic recommendations coexist?
- What happens when a brand’s agent, Walmart’s agent, and third-party agents interact directly?
- What new business models, marketplaces, protocols, or monetization mechanisms become possible?
You will translate these questions into a clear product thesis, multi-year roadmap, investment strategy, and measurable business outcomes.
Build the next generation of advertising experiences
Lead the development of new advertising products for Walmart’s emerging AI and agentic surfaces, including experiences such as Sparky.
Partner closely with teams defining the broader customer experience so that advertising is designed alongside the organic experience from the beginning, rather than layered in after the experience has already been created.
Experiment with new formats that may extend beyond conventional sponsored search, display, and prompts, including conversational commerce, sponsored recommendations, agent-assisted discovery, dynamic brand experiences, agent-to-agent advertising interactions, and formats that do not yet exist today.
Create experiences that are additive to the customer journey and establish clear principles for relevance, transparency, control, measurement, and trust.
Extend Walmart advertising into the agentic ecosystem
Define how Walmart Connect participates as commerce increasingly moves beyond Walmart-owned interfaces.
Develop the product strategy for advertising across external AI platforms, partner experiences, and emerging agent ecosystems.
Determine how Walmart’s unique assets, including commerce intent, first-party data, measurement, audiences, merchant relationships, and closed-loop transaction signals, can power differentiated advertising capabilities across these environments.
Work closely with partnerships, engineering, business development, legal, privacy, and enterprise AI teams to determine where Walmart should build, partner, enable, or establish new platform capabilities.
Shape agent-to-agent commerce and advertising
Anticipate a future where software agents increasingly transact with other software agents on behalf of consumers and businesses.
Define the advertising product strategy for this environment, including how advertiser objectives, product information, offers, budgets, bids, customer context, relevance, and measurement might operate when machines are making or assisting decisions.
Help define the APIs, protocols, platforms, decisioning systems, and marketplace mechanics required to make Walmart an important participant in the emerging agentic commerce ecosystem.
Build platforms, not one-off experiences
Partner with Engineering, Design, Data Science, Measurement, Identity, Developer Experience, and advertising product teams to create reusable capabilities that can support many agentic experiences.
Think horizontally across customer surfaces, advertising products, markets, partners, and future interfaces.
Ensure architectural decisions support interoperability, experimentation, global scale, responsible AI, and rapid evolution as the technology landscape changes.
Create the experimentation and measurement framework
Because many of these products will have no established industry playbook, build a rigorous approach to learning.
Define hypotheses, prototypes, experiments, customer research, advertiser validation, and measurement frameworks that allow Walmart to learn quickly while maintaining a high bar for customer trust.
Establish how success should be measured across customer utility, advertiser outcomes, incrementality, revenue, engagement, trust, and long-term ecosystem health.
Know when to scale an idea and when the evidence says to stop.
Lead across organizational boundaries
Agentic Advertising will sit at the intersection of some of Walmart’s most important strategic initiatives.
Build deep partnerships across Walmart Connect, eCommerce, Enterprise AI, Data Ventures, Marketplace, Engineering, Design, Data Science, Partnerships, Legal, Privacy, and Corporate Strategy.
Create clarity in areas where ownership will not always be obvious.
Influence senior executives through strong product judgment, structured narratives, customer insight, technical understanding, and data.
Build and lead an exceptional team
Build a high-caliber product organization capable of operating at the frontier of AI, advertising, and commerce.
Attract leaders who are technically curious, commercially sophisticated, customer-obsessed, and comfortable working in highly ambiguous environments.
Create a culture that combines imagination with execution rigor.
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Minimum Qualifications...
Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.
Option 1: Bachelor’s degree in computer science, engineering, or related area and 15 years’ experience in product management. Option 2: 17 years’ experience in product management or related area.Preferred Qualifications...
Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.
Master’s degree in Computer Science, Engineering, Business Administration or related area and 14 years’ experience as a product manager, ideally working on consumer-facing, large-scale, highly complex B2B/C products, Supervisory experience, We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart’s accessibility standards and guidelines for supporting an inclusive culture. Masters: Business Administration, Masters: Computer Engineering, Masters: Computer SciencePrimary Location...
850 Cherry Ave, San Bruno, CA 94066-3031, United States of America Walmart and its subsidiaries are committed to maintaining a drug-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.
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 Walmart, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
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.
Walmart AI Hiring
Walmart has 11 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span San Bruno, CA, US, Sunnyvale, CA, US, Bentonville, AR, US. Compensation range: $286K - $338K.
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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