Distinguished Software Engineer, AI/ML Engineer, Agentic Systems

Walmart

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Sunnyvale, CA
Salary
$169,000–$338,000 / yr
Employment
Full-time
Posted
43 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $208k
This role $254k
$128k $361k
below market most similar roles pay here above market

This role pays more than 81% of similar roles. Most pay $162,000–$253,500 — the blue band above. At the midpoint, this role pays about $254k versus about $208k 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 743 open roles on FindRole.

Listed pay typically runs $117,000–$234,000 across 541 roles with salary data.

Most-posted roles

View all roles at Walmart

At a glance

TL;DR · Distinguished Software Engineer, AI/ML Engineer, Agentic Systems

Role Posting Log in to save

JOB TITLE: Distinguished, Software Engineer -AI/ML Engineer – Agentic Systems As a Distinguished AI/ML Engineer within the Reliability Engineering Organization, you will lead the technical development of next-generation agentic AI systems and intelligent automation solutions. You will architect and implement multi-agent orchestration platforms, self-healing infrastructure, and ML-driven observability tools to manage complex reliability workflows, predictive failure analysis, and capacity planning. Your daily work involves building autonomous agents for change management and performance optimization across e-commerce, supply chain, and in-store systems. You will utilize technologies including TensorFlow, PyTorch, Large Language Models, Reinforcement Learning, and Computer Vision. Additionally, you will leverage cloud engineering expertise in Azure, GCP, and AWS, alongside Kubernetes, Docker, and MLOps platforms like MLflow or Kubeflow. This role solves critical reliability challenges by automating incident response and ensuring high availability across distributed systems.

Skills

AI/ML Agentic AI TensorFlow PyTorch Kubernetes Docker AWS Azure GCP MLOps AIOps CI/CD Kafka Pulsar MLflow Kubeflow Seldon Distributed Systems Infrastructure as Code Service Mesh NLP Computer Vision

What you'll do

  • Architect and develop advanced agentic AI systems to autonomously manage reliability engineering workflows and predictive failure analysis.
  • Design multi-agent orchestration platforms for automated change management, capacity planning, and performance optimization.
  • Build intelligent observability platforms using ML-driven anomaly detection and autonomous resolution across the technology landscape.
  • Develop self-healing infrastructure platforms that use AI to predict and automatically remediate system issues.
  • Build tools and automation to improve latency, availability, and scalability across mission-critical services.
  • Implement fault-tolerant systems on hybrid cloud infrastructure with a focus on autonomous recovery and intelligent failure prediction.
  • Drive the development of MLOps and AIOps platforms for continuous learning and autonomous optimization of reliability systems.
  • Provide technical mentorship and thought leadership through code reviews, design discussions, and knowledge sharing.

What we're looking for

  • Bachelor's degree in engineering, Computer Science, or a related field and 12+ years of experience in Reliability, AI/ML, or Platform Engineering.
  • Option 1: Bachelor's degree in computer science or related area and 6 years of software engineering experience.
  • Option 2: 8 years of experience in software engineering or a related area.
  • Expert-level AI/ML engineering experience with deep learning frameworks like TensorFlow and PyTorch for large-scale production deployments.
  • Advanced experience with agentic AI systems, including multi-agent frameworks, autonomous decision-making, and LLM-based agents.
  • Comprehensive Reliability Engineering expertise in service management, performance engineering, and capacity engineering for AI/ML systems.
  • Expert-level cloud engineering experience with Azure, GCP, or AWS, including Kubernetes, Docker, and serverless architectures.
  • MLOps and model lifecycle management using platforms such as MLflow, Kubeflow, or Seldon (preferred).

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