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About This Role
Position Summary...
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What you'll do...
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Essential Functions
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An individual must be able to successfully perform the essential functions of this position with or without a reasonable accommodation.
Tech. Problem Formulation: Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding;
Precedence and use cases; Business requirements and insights. To collaborate with multiple business stakeholders to gain an understanding of
complex organizational problems from varied perspectives to create effective technology focused solutions. Influence the business to redefine the
problem statement. Design multi\-stage, data\-driven solutions to real\-world problems for issues that are amenable to a data\-driven solution. Redefine
data analytics, big data analytics, automation goals, and deliverables by leveraging experience with the business problem. Identify return on
investment.
Understanding Business Context: Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices
across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in\-depth
knowledge of related practices; Directly relevant business metrics and business areas. To evaluate proposed business cases for projects and
initiatives. Translate business requirements into strategies, initiatives, and projects and aligns them to business strategy and objectives, and drives
the execution of deliverables. Build and articulate the business case and return on investment and delivers work that has demonstrable value.
Challenge business assumptions on topics related to one's domain expertise. Mentor the team members on new business insights and allied
developments. Proactively engage in the external community to build Walmart's brand and learn more about industry practices.
Analytical Modeling: Requires knowledge of feature relevance and selection; Exploratory data analysis methods and techniques; Advanced statistical
methods and best\-practice advanced modelling techniques (e.g., graphical models, Bayesian inference, basic level of NLP, Vision, neural networks,
SVM, Random Forest etc.); Multivariate calculus; Statistical models behind standard ML models; Advanced excel techniques and Programming
languages like R/Python; Basic classical optimization techniques (e.g., Newton\-Rapson methods, Gradient descent); Numerical methods of
optimization (e.g. Linear Programming, Integer Programming, Quadratic Programming, etc.) To explore and create automated feature generation
framework. Develop standard EDA process. Develop best practices on experimentation. Drive exploratory work in newer areas of Math, Statistics,
Machine Learning, and Optimization Techniques. Continuously improve the business's data analysis models. Create industry\-leading performance by
leveraging new and creative data\-sources. Employ the latest in machine learning in the department, Scope, design, and implement new machinelearning models to support the business's initiatives and programs with a view of achieving overall objectives and targets. Guide teams in the
development and delivery of big data predictive technologies models, and fully working prototypes of complex algorithms using readily available
libraries. Model Assessment and Validation: Requires knowledge of model fit testing, tuning, and validation techniques (e.g., Chi square, ROC curve, root
mean square error etc.); Impact of variables and features on model performance To identify and review model evaluation metrics based on analytical
requirements. Apply suitable techniques for model testing and tuning, to assess accuracy, fit, validity, and robustness. Ensure testing information is
documented and maintained by the team.
Model Deployment and Scaling: Requires knowledge of impact of variables and features on model performance; understanding of servers, model
formats to store models. To deploy models or model ensemble and ensure sustainability and maintenance overtime. Implement model monitoring and
model life\-cycle management practices. Assist in creation of innovative user interfaces and support the use of models through collaboration with key
stakeholders. Code Development and Testing: Requires knowledge of coding languages like SQL, Java, C\+\+, Python and others; Testing methods such as static,
dynamic, software composition analysis, manual penetration testing and others; Business, domain understanding. To write code to develop the
required solution and application features by determining the appropriate programming language and leveraging business, technical, and data
requirements. Create test cases to review and validate the proposed solution design. Create proofs of concept. Test the code using the appropriate
testing approach. Deploy software to production servers. Contribute code documentation, maintain playbooks, and provide timely progress updates.
Data Visualization: Requires knowledge of Visualization guidelines and best practices for complex data types; Multiple data visualization tools (for
example, Python, R libraries, GGplot, Matplotlib, Ploty, Tableau, PowerBI etc.); Advanced visualization techniques/ tools; Multiple story plots and
structures (OABCDE); Communication \& influencing technique; Emotional intelligence. To identify and recommend the most suitable visualization
tools based on context. Generate appropriate graphical representations of data and model outcomes. Understand customer requirements to design
appropriate data representation for complex data sets and drive User Experience designers and User Interface engineers to build front end
applications. Define application design based on customer requirements. Build compelling stories based on context to integrate multiple pieces of
information into cohesive insights. Present to and influence audiences using the appropriate data visualization frameworks and conveys clear
messages through deep business and stakeholder understanding. Customize communication style based on stakeholders and leverages relationships to drive behavioral change. Guide and mentor junior associates on story types, structures, and techniques based on context. Data Strategy: Requires knowledge of understanding of business value and relevance of data and data enabled insights / decisions; Appropriate
application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage
and Scalability, etc.; Understanding of the methods and applications that unlock the monetary value of data assets. To understand, articulate,
interpret, and apply principles of the defined strategy to complex business problems. Leverage experiences and learnings to inform and influence
data strategy that typically spans one or more functions or domains. Provides overall direction by analyzing business objectives and customer needs; developing, communicating, building support for, and implementing
business strategies, plans, and practices; analyzing costs and forecasts and incorporating them into business plans; determining and supporting
resource requirements; evaluating operational processes; measuring outcomes to ensure desired results; identifying and capitalizing on improvement
opportunities; promoting a customer environment; and demonstrating adaptability and sponsoring continuous learning.
Develops and implements strategies to attract and maintain a highly skilled and engaged workforce by diagnosing capability gaps; recruiting,
selecting, and developing talent; supporting mentorship, workforce development, and succession planning; and leveraging the capabilities of new and
existing talent. Cultivates an environment where associates respect and adhere to company standards of integrity and ethics by integrating these values into all
programs and practices; developing consequences for violations or non\-compliance; and supporting the Open Door Policy.
Develops and leverages internal and external partnerships and networks to maximize the achievement of business goals by sponsoring and leading
key community outreach and involvement initiatives; engaging key stakeholders in the development, execution, and evaluation of appropriate
business plans and initiatives; and supporting associate efforts in these areas.
Leadership Expectations
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Respect the Individual:Builds high\-performing teams; seeks, and embraces differences in people, cultures, ideas and experiences; creates a
workplace where all associates feel seen, supported and connected through culture of belonging so associates thrive and perform; drives a positive
associate and customer/member experience for all. Respect the Individual:Creates a discipline and focus around developing talent through feedback, coaching, mentoring, and developmental
opportunities; builds the talent pipeline, fosters an environment allowing everyone to bring their best selves to work, empowers associates and
partners to act in the best interest of the customer and company, and regularly recognizes others’ contributions and accomplishments; supports
strategies and drives initiatives that attract and retain the best talent. Respect the Individual:Builds strong and trusting relationships with team members and business partners; works collaboratively and cross\-functionally
to achieve objectives; and communicates and listens attentively, with energy and positivity to motivate, influence, and inspire commitment and action. Act with Integrity:Maintains and promotes the highest standards of integrity, ethics and compliance; models the Walmart values and leads by example
to foster our culture; supports Walmart’s goal of becoming a regenerative company by taking action to advance opportunity, sustainability, community,
and integrity (e.g., creating fair opportunities for associates and suppliers, driving local giving efforts). Act with Integrity:Ensures that teams follow the law, our code of conduct and company policies; promotes an environment where associates feel
comfortable sharing concerns and models our culture of non\-retaliation; listens to concerns raised by associates and takes action and enables others
to do the same; holds self and teams accountable for achieving results in a way that is consistent with our values. Act with Integrity:Acts as an altruistic servant leader and is consistently humble, self\-aware. Serve our Customers and Members:Delivers expected business results while putting the customer/member first and consistently applying an omnimerchant mindset and acting with an Every Day Low\-Cost mindset to drive value and Every Day Low Prices for customers/members. Serve our Customers and Members:Adopts a holistic perspective that considers data, analytics, customer/member insights, and different parts of the
business when making plans and implementing strategies. Strive for Excellence:Consistently raises the bar and seeks to improve; demonstrates curiosity and a growth mindset; seeks feedback, asks thoughtful
questions, and fosters an environment that supports learning, innovation, learning from mistakes, and intelligent risk\-taking; and exhibits resilience in
the face of setbacks. Strive for Excellence:Drives continuous improvements, supervises the adoption of new technology, and supports digital disruption in line with
Walmart’s business model.
Physical Activities
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The following physical activities are necessary to perform one or more essential functions of this position.
- Reads information, often in small print.
- Visually verifies information, often in small print.
- Communicates effectively in person or by using telecommunications equipment.
- Creates documents, reports, etc., using a writing instrument (such as a pencil or pen) or electronic device.
- Enters and locates information on electronic device.
- Presents information to small or large groups and individuals.
- Observes associate, customer, or supplier behavior.
Travel
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Traveling is necessary to perform one or more essential functions of this position.
- Travels internationally to and from multiple facilities or work\-sites requiring extended overnight stay
Preferred Qualifications
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- PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, or
- related field.
- 10 years' experience in data science, machine learning, optimization models, or related field.
- Successful completion of one or more assessments in Python, Spark, Scala, or R.
- 8 years’ experience using open source frameworks (for example, scikit learn, tensorflow, torch).
- 5 years' 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.
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 $160,000\.00 \- $320,000\.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 Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 7
years' experience in an analytics related field. Option 2: Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science,
Information Technology or related field and 5 years' experience in an analytics related field. Option 3: 9 years' experience in an analytics or related
field.
3 years' supervisory experience.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.*
Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch), 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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2101 Se Simple Savings Dr, Bentonville, AR 72712\-4304, 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 $160K-$320K range is above the 75th percentile 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. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($240K) sits 32% above the category median. Disclosed range: $160K to $320K.
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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