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About This Role
Position Summary...
What you'll do...
Position Summary...
As a Staff Machine Learning Engineer, you will architect and lead the development of scalable, production-grade ML systems that address complex business challenges. This role requires designing robust ML architectures, driving innovation in advanced modeling techniques, and overseeing the full ML lifecycle from data sourcing to deployment and monitoring. You will provide technical leadership, mentor engineering and data science teams, and collaborate cross-functionally to ensure alignment with platform strategies. Your expertise will advance Walmart’s ML capabilities, delivering impactful solutions that enhance operational efficiency, customer and associate experiences
About the team
At FinTech, our team develops and scales AI and ML driven solutions for Finance enabling faster, smarter, more consistent and precise month end close process. We apply machine learning, statistical techniques, forecasting, computer vision and optimization techniques to solve complex financial challenges including anomalous sales posting, cash loss management, financial reconciliation and proactive insights through agentic tools. Our solutions span predictive AI, agentic AI, and perception AI through computer vision. The team combines deep technical expertise with strong business partnership to build robust, scalable systems that improve financial accuracy, operational efficiency, and decision-making across the organization.
What you'll do...
- Architect and guide scalable, production-grade machine learning systems integrated into customer/associate facing products.
- Lead end-to-end ML lifecycle execution, including data sourcing, feature engineering, model training, deployment, and monitoring.
- Partner with cross-functional teams to align ML solutions with broader platform strategies, ensuring effective integration, scalability, and long-term sustainability.
- Design and build scalable, reliable infrastructure for deploying and serving computer vision models, leveraging containerization, GPU orchestration, and inference optimization frameworks such as NVIDIA TensorRT.
- Drive end-to-end performance benchmarking, capacity planning, and cost optimization of ML workloads. Partner with MLOps and infrastructure teams to establish best practices for GPU utilization, model serving, monitoring, and production reliability.
- Champion advanced MLOps practices such as CI/CD pipelines, model versioning, drift detection, and performance telemetry.
- Mentor engineers, influence architectural decisions, and promote engineering excellence across teams.
Evaluate emerging infrastructure patterns, model paradigms and platform capabilities into a forward-looking roadmap. *
What you'll bring:
- Extensive experience architecting scalable, production-grade machine learning systems and platforms.
- Hands-on experience designing, developing, and deploying computer vision solutions, including object detection, multi-object tracking, action recognition, and video analytics, using deep learning architectures and frameworks such as PyTorch, OpenCV, and YOLO.
- Proven experience building and optimizing end-to-end computer vision pipelines, including video preprocessing, model training, fine-tuning, GPU-accelerated inference, and deployment, with a focus on accuracy, latency, throughput, and scalability.
- Deep knowledge of neural networks, deep learning, natural language processing, reinforcement learning, and time series analysis.
- Hands-on experience building systems that support advanced agentic architectures, such as memory, trace, eval, orchestration and routing layers for complex model systems.
- Proven ability to lead technical strategy, mentor engineers, and influence cross-functional collaboration.
- Excellent critical thinking and technical writing capabilities to communicate complex concepts clearly.
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.
Sunnyvale, California US-11789: The annual salary range for this position is $169,000.00 - $338,000.00
Bentonville, Arkansas US-09050: The annual salary range for this position is $130,000.00 - $260,000.00
Additional compensation includes annual or quarterly performance bonuses.
Additional compensation for certain positions may also include :
- Stock
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Minimum Qualifications...
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, computer engineering, computer information systems, software engineering, or related area and 4 years’ experience in software engineering, machine learning engineering, AI systems or related area.
Option 2: 6 years’ experience in software engineering, machine learning engineering, AI systems 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, computer engineering, computer information systems, software engineering, or related area and 2 years' experience in software engineering, machine learning engineering, AI systems or related area., Prior work experience in accessibility best practices., Prior work experience 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.Primary Location...
809 11th Ave, Sunnyvale, CA 94089-4731, 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 $130K-$338K 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 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 $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 ($234K) sits 10% above the category median. Disclosed range: $130K to $338K.
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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