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About This Role
Position Summary...
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What you'll do...
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As a Staff Software Engineer, you'll be a technical leader who defines the direction for and evolves the backend microservices, data pipelines, and ML\-serving infrastructure that power search at massive scale. You'll lead a team of 6–10 engineers, set the technical vision for critical systems, create clarity from ambiguity on complex cross\-functional initiatives, and drive the quality bar across the team. You'll spend your days writing and reviewing code, leading design discussions, and making the architectural decisions that shape the next generation of Walmart Search. Your work will shape how hundreds of millions of customers discover products every day.
We're in an active phase of platform modernization — redesigning and refactoring core systems. If you want to build, not just maintain, this is the right time to join. About Team:
The eCommerce Search engineering team owns the end\-to\-end technology stack that powers product search and discovery across Walmart's global eCommerce channels, backed by microservices, large\-scale data and feature pipelines, search engines, and ML model serving infrastructure. We handle millions of queries per day, and every improvement to our systems directly impacts how customers find what they need. Our systems tackle an advanced set of problems in the search domain:
- Query \& Intent Understanding — Query classification, product type prediction, intent recognition, and sequence tagging using classical ML, NLP, and deep learning techniques.
- Autocomplete — Real\-time query suggestions at low\-latency, high\-throughput scale.
Retrieval \& Search Execution — Complex query construction, faceted navigation, semantic and vector\-based retrieval, and result orchestration across search engines.
- Multi\-phase Ranking — ML\-powered ranking models that optimize for customer satisfaction and business outcomes across multiple ranking stages, using learn to rank and neural models.
- Data \& Feature Pipelines — Large\-scale pipelines that feed search indices, feature stores, and analytics platforms.
What you'll do:
- Define the technical direction and drive the architecture for mission\-critical search microservices — spanning Core Orchestration, Query Understanding, Autocomplete, Facet \& Navigation, Ranking, and more.
- Design, build, and optimize high\-throughput, low\-latency backend services, applying best practices around distributed systems, fault tolerance, horizontal scalability, concurrency, and performance tuning.
- Design and build high\-scale data and feature pipelines that process data through transformation and aggregation layers into downstream data stores, search indices, and feature stores.
- Architect complex query patterns and integrations with search engines to power relevance, ranking, and retrieval at scale.
- Lead discovery and design phases for medium\-to\-large initiatives — partnering with product management, data science, and UX to translate business requirements into scalable technical solutions; build cross\-functional alignment, drive proof\-of\-concepts, and validate ideas through prototypes.
- Design and run A/B experiments to validate search improvements; use data\-driven analysis and continuous monitoring to measure the impact on customer engagement and business metrics.
- Technically lead a team of 6–10 engineers, including collaboration with offshore/distributed team members — providing architectural guidance, conducting design and code reviews, identifying and removing blockers, and setting the quality bar for the team.
- Mentor and grow engineers across experience levels; drive a culture of engineering excellence through knowledge\-sharing, disciplined testing practices, and thoughtful documentation.
- Collaborate with data scientists to productionize ML models for ranking and query understanding; contribute to MLOps practices, feature store development, and model serving optimization for latency and throughput in production.
- Build and maintain observability, monitoring, and alerting for search services; participate in on\-call rotations and own the reliability of Search platform services in production — troubleshoot issues with urgency, perform root cause analysis, and build preventive measures.
- Contribute to long\-range technical roadmaps and communicate technical strategy to senior leadership; resolve cross\-team technology disagreements through informed discussion and represent the team in org\-level architectural decisions.
Stay current with industry research and emerging technologies in search, distributed systems, and ML to inform the team's technical direction.
- Work boldly with a sense of urgency — embrace mistakes, learn from them quickly, and drive the team toward success.
What you'll bring:
- 10\+ years, of experience designing, developing, and shipping production code in large\-scale distributed systems.
- Deep expertise in backend development with Java and Spring Boot — strong fundamentals in object\-oriented design, concurrency, and performance optimization.
- Proven track record building cloud\-native, high\-throughput microservices — including RESTful API design, fault tolerance patterns, and horizontal scaling — deployed on Kubernetes.
- Hands\-on experience designing and building data and feature pipelines at scale using Apache Spark (including SparkSQL), working with streaming platforms like Kafka, and NoSQL databases like Cassandra.
- Demonstrated ability to technically lead teams of 6–10 engineers — setting architectural direction, mentoring, and raising the quality bar through design and code reviews.
- A disciplined, test\-driven approach to development — you're well\-versed in testing frameworks (JUnit, Mockito) and bring a genuine commitment to code quality, testability, and documentation.
- Strong communication skills — able to create clarity from ambiguity, articulate technical trade\-offs to both engineering peers and non\-technical stakeholders, and influence across teams.
- Proficiency in big data processing and analytics using technologies such as Hive, BigQuery, GCP/GCS, or equivalent.
- Familiarity with NoSQL databases (Cassandra, Cosmos DB), distributed caching, and cloud object storage (Azure Blob Storage).
- Track record of refactoring, redesigning, or rewriting existing high\-scale applications is a plus — we are actively modernizing our search platform.
Preferred Qualifications:
- Experience with search engines (Solr, Elasticsearch, Vespa) or similar information retrieval systems, including vector search and embedding\-based retrieval (e.g., FAISS, ANN indexes), and familiarity with search domain concepts such as query understanding, retrieval strategies, multi\-phase ranking, and relevance optimization.
- Background in machine learning — including core concepts (classification, regression, neural networks, transformer\-based models, LLMs), hands\-on experience with frameworks (PyTorch, TensorFlow, scikit\-learn, XGBoost), and an understanding of the full model lifecycle from training through production serving.
- Hands\-on work with ML infrastructure in production — including MLOps practices, model serving optimization (dynamic batching, TensorRT, ONNX, quantization), and building training and inference pipelines at scale.
- Proficiency in Python for data engineering, ML prototyping, or production scripting.
- Familiarity with cloud data platforms and orchestration tools such as GCP, BigQuery, and Airflow.
About Walmart Global Tech
Imagine working in an environment where one line of code can make life easier for hundreds of millions of people. That's what we do at Walmart Global Tech. We're a team of software engineers, data scientists, cybersecurity experts, and service professionals within the world's leading retailer who make an epic impact and are at the forefront of the next retail disruption. People are why we innovate, and people power our innovations. We are people\-led and tech\-empowered. We train our team in the skillsets of the future and bring in experts like you to help us grow. We have roles for those chasing their first opportunity as well as those looking for the opportunity that will define their career. Here, you can kickstart a great career in tech, gain new skills and experience for virtually every industry, or leverage your expertise to innovate at scale, impact millions, and reimagine the future of retail.
Flexible, hybrid work:
We use a hybrid way of working that is primarily in office coupled with virtual when not onsite. Our campuses serve as a hub to enhance collaboration, bring us together for purpose, and deliver on business needs. This approach helps us make quicker decisions, remove location barriers across our global team, and be more flexible in our personal lives.
Benefits:
Beyond our great compensation package, you can receive incentive awards for your performance. Other great perks include 401(k) match, stock purchase plan, paid maternity and parental leave, PTO, multiple health plans, and much more.
Equal Opportunity Employer:
Walmart, Inc. is an Equal Opportunity Employer – By Choice. We believe we are best equipped to help our associates, customers, and the communities we serve live better when we really know them.
The above information has been designed to indicate the general nature and level of work performed in the role. It is not designed to contain or be interpreted as a comprehensive inventory of all responsibilities and qualifications required of employees assigned to this job. The full Job Description can be made available as part of the hiring process.
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 $143,000\.00 \- $286,000\.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, computer engineering, computer information systems, software engineering, or related area and 4 years’ experience in software engineering or related area.
Option 2: 6 years’ experience in software engineering 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.*
Master’s degree in Computer Science, Computer Engineering, Computer Information Systems, Software Engineering, or related area and 2 years' experience in software engineering or related area, 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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815 Eleventh 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 $143K-$286K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($214K) sits 28% above the category median. Disclosed range: $143K to $286K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Walmart AI Hiring
Walmart has 36 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Bentonville, AR, US, Sunnyvale, CA, US, Elwood, IL, US. Compensation range: $79K - $370K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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