Walmart
This range is provided by Walmart. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $169,000.00/yr - $338,000.00/yr
Position summary The role focuses on designing and delivering production‑grade machine learning solutions that directly impact Walmart’s business. It requires deep expertise in ML architecture, MLOps, and cloud‑scale operations while mentoring a team of engineers.
What you’ll do
ML System Architecture & Delivery : Lead the design and implementation of scalable, production‑grade ML solutions that address business‑critical needs; translate complex or ambiguous problem statements into clear technical requirements, architectural designs, and success metrics; select and integrate appropriate ML frameworks (Scikit‑learn, TensorFlow, PyTorch, XGBoost, Spark ML) and coding languages (Python, SQL, Java, C++, R); drive platform alignment by ensuring solutions leverage and contribute to Walmart’s shared ML infrastructure and tooling.
End‑to‑End ML Lifecycle Execution : Oversee and contribute to all phases of the ML lifecycle—including data sourcing, feature engineering, model training, deployment, monitoring, and continuous improvement; apply MLOps best practices such as CI/CD for ML, automated training pipelines, model versioning, and telemetry‑based monitoring; implement robust evaluation frameworks for model performance, data quality, and fairness, ensuring compliance with responsible AI principles.
ML Platform Engineering & Operations : Build and maintain reusable infrastructure components, including feature stores, model runtimes, SDKs, and workflow orchestration systems; manage large‑scale ML operations across cloud environments (AWS, GCP, Azure), leveraging containerization, Kubernetes, and workflow automation; implement observability tooling to track model behavior, detect drift, and ensure high availability and low latency in production environments.
Technical Leadership & Mentorship : Provide hands‑on mentorship to engineers, guiding them through architectural decisions, implementation details, and debugging complex ML issues; set and enforce engineering standards for code quality, testing, deployment, and operational excellence; foster a culture of technical depth, peer learning, and collaboration across teams.
Experimentation & Innovation : Design and manage experimentation pipelines for hypothesis testing, A/B experiments, and continuous model iteration; explore advanced techniques such as AutoML, neural architecture search, domain‑specific NLP tooling, and multimodal architectures to accelerate solution delivery; share innovations and learnings through technical documentation, presentations, and cross‑team forums.
Technical Ownership & Judgment : Demonstrate responsibility for deliverables, ensuring solutions meet quality standards and business objectives; apply sound reasoning to make informed technical decisions, balancing trade‑offs and considering long‑term impacts on system architecture and business goals; adapt quickly to change, embrace new technologies, anticipate downstream effects, and integrate feedback to improve outcomes.
Collaboration & Continuous Improvement : Work effectively across teams, openly share knowledge, and communicate complex ideas clearly to both technical and non‑technical stakeholders; proactively seek opportunities to enhance processes, tools, and products; encourage experimentation and learning to drive ongoing improvement.
Minimum qualifications
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.
OR 6 years of experience in software engineering, machine learning engineering, AI systems or related area.
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.
Experience creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly.
Location 1395 Crossman Ave, Sunnyvale, CA 94089‑1114, United States of America
Benefits At Walmart, we offer competitive pay, performance‑based bonus awards, and a comprehensive benefits package, including medical, vision, dental, 401(k), stock purchase, paid time off, health and financial benefits, and a Walmart‑paid education benefit program. Additional details and eligibility conditions are available on One.Walmart.
EEO & Workplace policy Walmart and its subsidiaries are committed to maintaining a drug‑free workplace and have 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.
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Base pay range $169,000.00/yr - $338,000.00/yr
Position summary The role focuses on designing and delivering production‑grade machine learning solutions that directly impact Walmart’s business. It requires deep expertise in ML architecture, MLOps, and cloud‑scale operations while mentoring a team of engineers.
What you’ll do
ML System Architecture & Delivery : Lead the design and implementation of scalable, production‑grade ML solutions that address business‑critical needs; translate complex or ambiguous problem statements into clear technical requirements, architectural designs, and success metrics; select and integrate appropriate ML frameworks (Scikit‑learn, TensorFlow, PyTorch, XGBoost, Spark ML) and coding languages (Python, SQL, Java, C++, R); drive platform alignment by ensuring solutions leverage and contribute to Walmart’s shared ML infrastructure and tooling.
End‑to‑End ML Lifecycle Execution : Oversee and contribute to all phases of the ML lifecycle—including data sourcing, feature engineering, model training, deployment, monitoring, and continuous improvement; apply MLOps best practices such as CI/CD for ML, automated training pipelines, model versioning, and telemetry‑based monitoring; implement robust evaluation frameworks for model performance, data quality, and fairness, ensuring compliance with responsible AI principles.
ML Platform Engineering & Operations : Build and maintain reusable infrastructure components, including feature stores, model runtimes, SDKs, and workflow orchestration systems; manage large‑scale ML operations across cloud environments (AWS, GCP, Azure), leveraging containerization, Kubernetes, and workflow automation; implement observability tooling to track model behavior, detect drift, and ensure high availability and low latency in production environments.
Technical Leadership & Mentorship : Provide hands‑on mentorship to engineers, guiding them through architectural decisions, implementation details, and debugging complex ML issues; set and enforce engineering standards for code quality, testing, deployment, and operational excellence; foster a culture of technical depth, peer learning, and collaboration across teams.
Experimentation & Innovation : Design and manage experimentation pipelines for hypothesis testing, A/B experiments, and continuous model iteration; explore advanced techniques such as AutoML, neural architecture search, domain‑specific NLP tooling, and multimodal architectures to accelerate solution delivery; share innovations and learnings through technical documentation, presentations, and cross‑team forums.
Technical Ownership & Judgment : Demonstrate responsibility for deliverables, ensuring solutions meet quality standards and business objectives; apply sound reasoning to make informed technical decisions, balancing trade‑offs and considering long‑term impacts on system architecture and business goals; adapt quickly to change, embrace new technologies, anticipate downstream effects, and integrate feedback to improve outcomes.
Collaboration & Continuous Improvement : Work effectively across teams, openly share knowledge, and communicate complex ideas clearly to both technical and non‑technical stakeholders; proactively seek opportunities to enhance processes, tools, and products; encourage experimentation and learning to drive ongoing improvement.
Minimum qualifications
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.
OR 6 years of experience in software engineering, machine learning engineering, AI systems or related area.
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.
Experience creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly.
Location 1395 Crossman Ave, Sunnyvale, CA 94089‑1114, United States of America
Benefits At Walmart, we offer competitive pay, performance‑based bonus awards, and a comprehensive benefits package, including medical, vision, dental, 401(k), stock purchase, paid time off, health and financial benefits, and a Walmart‑paid education benefit program. Additional details and eligibility conditions are available on One.Walmart.
EEO & Workplace policy Walmart and its subsidiaries are committed to maintaining a drug‑free workplace and have 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.
#J-18808-Ljbffr