This role pays more than
90%
of similar roles. Most pay
$126,800–$189,187
— the shaded band above.
At the midpoint, this role pays about
$214k
versus about
$158k
for comparable roles.
Based on 240 similar postings.
Employer
About Walmart
Walmart Inc. is the world''s largest retailer by revenue, operating a chain of hypermarkets, discount department stores, and grocery stores, as well as a growing e-commerce presence through Walmart.com. Industry: General Merchandise & Grocery Retail
Walmart currently has
516 open roles
on FindRole.
Listed pay typically runs
$117,000–$234,000
across 510 roles with salary data.
As a Senior Applied Scientist at Walmart's Global Tech team, you will lead the design and delivery of advanced ML and GenAI systems to enhance merchant productivity and operational intelligence across various domains including Pricing, Forecasting, Store Operations, Transportation, and Supply Chain. Your responsibilities include building innovative AI-driven solutions using cutting-edge agentic frameworks, designing models that automatically investigate anomalies, and mentoring a team of scientists and engineers. You will also collaborate with cross-functional teams to translate complex business problems into impactful AI solutions. Ideal candidates have over six years of experience in shipping ML models to production at scale, expertise in core ML techniques and GenAI technologies like LLMs and RAG, proficiency in Python and distributed systems, and strong communication skills for technical leadership roles.
Lead the design and delivery of AI systems that enhance productivity in various Walmart domains.
Build and deploy GenAI applications to automate workflows and reduce operational time.
Investigate anomalies across merchandising, supply chain, and store operations using advanced models.
Integrate state-of-the-art techniques from LLMs, retrieval, and classical ML into projects.
Mentor team members and set technical standards for AI-driven solutions development.
What we're looking for
6+ years of experience as an Applied Scientist or Machine Learning Engineer in high-scale production environments.
Deep expertise in core ML techniques and hands-on experience with Generative AI, including LLMs, RAG, fine-tuning, evaluations, agent orchestration, and tool use.
Strong software engineering skills, including proficiency in Python, distributed systems, ML pipelines, CI/CD for ML, and model monitoring.
Experience working with large-scale data systems like Spark/Hive and cloud platforms such as GCP or Azure.
Proven ability to lead technical teams, mentor scientists and engineers, and set the technical direction for AI projects.