Machine Learning Engineer

About This Role

This Staff Machine Learning Engineer role involves architecting and leading the development of scalable, production-grade ML systems to solve complex business challenges. The position requires designing robust ML architectures, driving innovation in advanced modeling techniques, and overseeing the full ML lifecycle from data sourcing to deployment and monitoring. You will provide technical leadership, mentor engineering and data science teams, and collaborate cross-functionally to align with platform strategies, ultimately advancing Walmart’s ML capabilities and enhancing operational efficiency, customer, and associate experiences.

Responsibilities

• Architect and guide scalable, production-grade machine learning systems integrated into customer/associate facing products
• Lead end-to-end ML lifecycle execution, including data sourcing, feature engineering, model training, deployment, and monitoring
• Partner with cross-functional teams to align ML solutions with broader platform strategies, ensuring effective integration, scalability, and long-term sustainability
• Design and build scalable, reliable infrastructure for deploying and serving computer vision models, leveraging containerization, GPU orchestration, and inference optimization frameworks such as NVIDIA TensorRT
• Drive end-to-end performance benchmarking, capacity planning, and cost optimization of ML workloads
• Partner with MLOps and infrastructure teams to establish best practices for GPU utilization, model serving, monitoring, and production reliability
• Champion advanced MLOps practices such as CI/CD pipelines, model versioning, drift detection, and performance telemetry
• Mentor engineers, influence architectural decisions, and promote engineering excellence across teams
• Evaluate emerging infrastructure patterns, model paradigms, and platform capabilities into a forward-looking roadmap

Requirements

• Extensive experience architecting scalable, production-grade machine learning systems and platforms
• Hands-on experience designing, developing, and deploying computer vision solutions, including object detection, multi-object tracking, action recognition, and video analytics, using deep learning architectures and frameworks such as PyTorch, OpenCV, and YOLO
• Proven experience building and optimizing end-to-end computer vision pipelines, including video preprocessing, model training, fine-tuning, GPU-accelerated inference, and deployment, with a focus on accuracy, latency, throughput, and scalability
• Deep knowledge of neural networks, deep learning, natural language processing, reinforcement learning, and time series analysis
• Hands-on experience building systems that support advanced agentic architectures, such as memory, trace, eval, orchestration, and routing layers for complex model systems