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About This Role
Position Summary...
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Walmart Marketing is undergoing one of the most significant transformations in its history — modernizing how we measure, experiment, and deploy AI and technology to reach customers more effectively and efficiently. This Staff Technical Program Manager role sits at the center of that transformation, owning end\-to\-end program execution across three interconnected workstreams: Measurement (Next\-Gen Mixed\-Media Modeling, data taxonomy, attribution), Experimentation (A/B testing infrastructure, governance, and learning ambition framework), and AI \& MarTech (agentic AI systems, prototype\-to\-production pipelines, MarTech fitness, and marketing workflow automation).
You will operate as the connective tissue between data scientists, ML engineers, marketing analysts, MarTech engineers, and executive stakeholders — translating complex technical work into clear business outcomes and ensuring that critical FY26/FY27 milestones are delivered on time, with measurable impact. This is not a seat\-warmer role. You will drive the engine.What you'll do...
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1\. Transformation Program Ownership End\-to\-End Workstream Program Management: Own and maintain program plans, milestone tracking, dependency maps, and status reporting across the Measurement, Experimentation, and AI \& MarTech workstreams. Drive quarterly planning cycles, define critical\-path milestones, and coordinate “inform” and “decision” moments with workstream sponsors and executive leadership. Impact Metrics \& Measurement Standards: Drive development and operationalization of Marketing Transformation's KPI taxonomy — anchored to Associate Hours Reclaimed (AHR) and marketing effectiveness metrics. Build and maintain the monthly impact dashboard and deliver leadership\-ready reporting on transformation ROI. 2\. Technical Cross\-Functional Coordination Cross\-Team Alignment \& Delivery: Serve as the connective tissue across data scientists, ML engineers, data engineers, MarTech engineers, and marketing analysts — facilitating design discussions, resolving SME access issues, clarifying ownership gaps, and ensuring alignment on technical architecture and delivery commitments. Partner within the Walmart PM/EM/TPM Triad model to balance business and engineering priorities. Data Quality \& Pipeline Governance: Collaborate with data scientists, ML engineers, and data engineers to ensure high data quality, model accuracy, and system performance. Coordinate delivery of data pipeline automation and AI\-powered validation capabilities — including anomaly detection for MM data pipelines and next\-gen MM pipeline enhancements. MarTech Fitness \& Data Readiness: Coordinate the MarTech Fitness Audit (Phase 1 and Phase 2 post\-taxonomy), tool\-by\-tool capability assessment, and prioritized recommendations delivery. Support the Marketing Data Readiness Scorecard development and data governance operating model definition. 3\. Stakeholder Communication \& Executive Reporting Executive Briefings \& Reporting: Translate technical systems language and KPIs (e.g., model Accuracy, Precision, Recall, RMSE, AHR baselines) into business communication updates and project success metrics for executive Marketing leadership. Develop and maintain weekly/bi\-weekly written status reporting, milestone trackers, and executive readout decks. Change Management \& Enablement Comms: Drive change management communication planning across taxonomy launches, next\-gen model rollouts, and experimentation tool deployments — including audience segmentation, comms timing, launch announcements, onboarding trainings, and self\-serve FAQs. Partner with the Marketing AI Knowledge Network to align enablement comms across ARC Hours, newsletter, and AI Spotlight Sessions. 4\. Risk Management \& Process Development Proactive Risk Identification \& Mitigation: Proactively identify and manage risks related to data privacy, model bias, AI governance, performance degradation, timeline slippage, and resource constraints. Own risk logging, mitigation planning, and escalation paths. Ensure ethical and responsible AI deployment across marketing measurement and agentic systems. Tools, Workflows \& Agile Process Development: Utilize AI\-powered project management tools (Jira, Confluence, Asana) to automate documentation, track milestones, and visualize project velocity. Drive Agile/Scrum execution — backlog grooming, sprint planning, retrospectives, and Scrum of Scrums. Lead Workflow Census mapping to identify and prioritize marketing workflow automation opportunities. REQUIRED QUALIFICATIONS:
- Experience: 5\+ years as a Technical Program Manager, AI/Data Program Manager, or equivalent technical program management role; ideally with direct AI/ML or data science project ownership.
- AI/ML Fluency: Solid working understanding of machine learning concepts — supervised/unsupervised learning, neural networks, LLMs, Bayesian modeling, and causal inference frameworks. Familiarity with model evaluation metrics (Accuracy, Precision, Recall, RMSE).
- Data Engineering Awareness: Familiarity with data pipeline architecture, data lake/CDP integration patterns, data validation practices, and model data preparation workflows.
- Marketing Technology: Experience working with modern marketing stacks, CRM/CDP data, marketing measurement systems (MMM, multi\-touch attribution), and experimentation platforms.
- Agile Leadership: Proven expertise leading Agile/Scrum teams — backlog grooming, sprint planning, retrospectives, and Scrum of Scrums across multi\-team programs.
- Tooling: Proficient with Jira and Confluence. Working familiarity with Python (readable, not writable), SQL, and cloud platforms (AWS, GCP, or Azure).
- Communication: Exceptional skill translating technical complexity (model metrics, infrastructure decisions, engineering tradeoffs) into executive\-ready business language and narrative.
- Stakeholder Management: Demonstrated ability to manage cross\-functional stakeholders across data science, engineering, product, marketing, and executive leadership simultaneously.
- Risk Management: Experience identifying, logging, and driving mitigation for technical risks including data quality issues, model drift, privacy concerns, and delivery delays.
PREFERRED QUALIFICATIONS:
- Marketing Analytics Depth: Background in marketing analytics, econometrics, marketing mix modeling, or behavioral/causal modeling.
- MLOps Experience: Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow) and model deployment lifecycle management.
- Agentic AI Exposure: Experience supporting development or deployment of AI agents, multi\-agent orchestration systems, or LLM\-powered workflow automation.
- Experimentation Platforms: Experience with A/B testing infrastructure, experimentation governance frameworks, or statistical significance frameworks used in marketing contexts.
- MarTech Stack Knowledge: Familiarity with MarTech auditing, CRM/CDP platforms, media buying systems, tagging infrastructure, and marketing data taxonomy design (e.g., Claravine).
- Change Management: Experience designing and executing change management plans for technical tool rollouts to non\-technical audiences at scale.
- Executive Storytelling: Demonstrated ability to build insight and storytelling frameworks that translate model outputs into compelling business narratives for CMO\-level audiences.
- Certifications: PMP, Agile Scrum Master (CSM/SAFe), or equivalent program management certification.
At Walmart, we offer competitive pay as well as performance\-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company\-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short\-term and long\-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more. You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable. For information about PTO, see https://one.walmart.com/notices. Live Better U is a Walmart\-paid education benefit program for full\-time and part\-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short\-form certificates. Tuition, books, and fees are completely paid for by Walmart.
Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.
For information about benefits and eligibility, see One.Walmart.
The annual salary range for this position is $110,000\.00 \- $220,000\.00 Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include :
- Stock
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Minimum Qualifications...
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*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, information technology, engineering, or related area and 6 years’ experience in engineering, engineering program management, technical program management, product management, or related area.
- Option 2: 8 years’ experience in engineering, engineering program management, technical program management, product management, or related area.
Preferred Qualifications...
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*Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.*
Certification in Project Management., Master’s degree in Business Administration, with specialization in strategy, supply chain, finance, information systems, or related area and 4 years’ experience in product design., Supervisory, 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.Primary Location...
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1601 SE 10th St, Bentonville, AR 72716, 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.
Salary Context
This $110K-$220K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 9% below the category median. Disclosed range: $110K to $220K.
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.
Walmart AI Hiring
Walmart has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, Data Engineer. Positions span Bellevue, WA, US, Bentonville, AR, US, Sunnyvale, CA, US. Compensation range: $216K - $320K.
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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