This role pays less than
82%
of similar roles. Most pay
$173,200–$263,087
— the shaded band above.
At the midpoint, this role pays about
$165k
versus about
$218k
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
333 open roles
on FindRole.
Listed pay typically runs
$117,000–$234,000
across 268 roles with salary data.
As a Principal, Data Scientist, Agentic AI Systems Engineering & Model Post-Training, you will join the Supply Chain AI Lab & Innovation Factory to build production-grade agentic AI systems. You will independently own major product domains, designing and operating full-stack systems including agent orchestration, backend services, APIs, and React/TypeScript interfaces. Your daily work involves building multi-agent harnesses with long-horizon planning, tool use, and durable sessions while improving models through reinforcement learning, supervised fine-tuning, and distillation. You will utilize frameworks like Pydantic AI, LangGraph, LangChain, AutoGen, and LlamaIndex alongside technologies like Kafka, Flink, Spark, and Google Cloud Platform. This role solves complex supply-chain problems by developing the Autonomous Supply Chain Engine, specifically focusing on the Discovery Loop to monitor signals and generate strategic recommendations.
What does a Data Scientist earn?
Median $171650 from 280 postings across 62 companies.
Design, build, and operate production-grade agentic AI systems with multi-step planning and tool use.
Develop a policy-first agent runtime with deterministic access controls, identity authorization, and auditable human approvals.
Build full-stack components including backend APIs, database schemas, streaming pipelines, and React/TypeScript interfaces.
Engineer multi-agent harnesses and orchestration layers using frameworks like LangGraph, Pydantic AI, or custom infrastructure.
Create governed knowledge infrastructure using vector retrieval, knowledge graphs, and relational models for durable agent memory.
Improve model reasoning through hands-on post-training techniques like RLHF, RLAIF, and supervised fine-tuning.
Establish AgentOps practices for prompt versioning, tracing, evaluation datasets, and cost/latency controls.
Mentor senior engineers and lead technical architecture reviews to improve team performance and platform capabilities.
What we're looking for
8+ years of hands-on software, machine learning, or applied AI engineering experience with Staff/Principal-level technical impact.
Bachelor's degree in a related field with 4 years of software engineering experience, or 6 years of software engineering experience.
Master's degree in a related field with 3 years of analytics experience, or 7 years of experience in an analytics or related field.
Strong experience in agentic AI engineering, including multi-agent orchestration, tool use, and long-horizon planning using frameworks like LangGraph or AutoGen.
Hands-on experience with model post-training techniques including RLHF, RLAIF, supervised fine-tuning, and distillation.
Proficiency in Python and at least one production backend language such as Go, Java, or TypeScript.
Full-stack development capability including React/TypeScript for frontends, APIs, and data layer design.
Experience with distributed systems, GCP, Kubernetes/Docker, Kafka, Flink, Spark, and MLOps practices.
Master's or Ph.D. in a related field (preferred).
Experience building developer-facing agentic platforms, SDKs, or coding and automation agents (preferred).
Knowledge of graph and knowledge-representation, including ontology design and multi-hop retrieval (preferred).
Experience with ML platform engineering, model serving, and production LLM quality evaluation (preferred).
Successful completion of one or more assessments in Python, Spark, Scala, or R (preferred).
LLM
Agentic Workflows
Python
SQL
Spark
Scala
R
scikit learn
TensorFlow
Data Engineering
Machine Learning
System Design
Evaluation Frameworks
Data Governance
WCAG) 2.2 AA
Large Language Models
Prompt Engineering
Computer Vision
Agentic AI
Python
Jupyter Notebooks
Posit Workbench
Posit Connect
Data Analytics
Curriculum Design
Knowledge Management
Scenario-Based Training
Standard Operating Procedure (SOP)
AI/ML Lifecycle