Staff Software Engineer, Machine Learning

Walmart

Staff Machine Learning Software Engineer building scalable computer vision and GenAI pipelines for Walmart, the world’s leading retailer. Deploying GPU-based models and production ML systems.

Posted 8/6/2026full-timeBangalore • 🇮🇳 IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining low-latency, high-throughput Computer Vision pipelines and deploying end-to-end ML and GenAI solutions. Proficient in leveraging big data analytics, MLOps techniques, and advanced programming skills to drive data-derived insights and improve application performance.

Highest-signal resume keywords

Machine Learning EngineeringComputer Vision ExpertiseNvidia DeepStream ConfigurationProduction Deployment of PyTorch ModelsKubernetes Operations

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningComputer VisionPython ProficiencyStatistical AnalysisSQL/NoSQL ConceptsMLOps TechniquesGStreamer DesignObject DetectionREST API DevelopmentTesting Frameworks

Soft Skills

CommunicationCollaborationSelf-Motivated Learning

Tools & Technologies

Nvidia Triton Inference ServerGitCloud Technologies (GCP, Azure)Helm ChartsCI/CDReactNode.jsSpring Boot

Industry Keywords

Big Data AnalyticsMLOpsData Quality DetectionLatency ProfilingAutomation

Tech Stack

Tools & technologies

AzureCloudGoogle Cloud PlatformJavaScriptKubernetesNode.jsNoSQLNumpyPandasPythonPyTorchRDBMSReactReact NativeSCSSSpringSpring BootSpringBootSQLTypeScript

About the role

Key responsibilities & impact

Build and maintain low-latency/high-throughput Computer Vision pipelines at Walmart scale.
Build reusable components and deploy end-to-end ML and GenAI pipelines.
Drive data-derived insights using statistical, machine learning, and computational algorithms.
Gather data, assess data validity, and synthesize large analytics datasets.
Communicate recommendations to business partners and influence future plans.
Automate Vision solutions using shell, API design, queuing, and advanced SQL/NoSQL concepts.
Use big data analytics and MLOps techniques to identify trends, patterns, and discrepancies.
Improve application performance and address web application and machine learning bottlenecks.
Apply computer vision and forecasting expertise.
Collaborate with product owners, data scientists, and engineers to deliver and deploy models at scale while ensuring code quality.

Requirements

What you’ll need

8–13 years of experience as a Machine Learning Engineer.
Minimum qualification: bachelor's degree in computer science, computer engineering, computer information systems, software engineering, or related area and 4 years’ software engineering experience; alternatively, 6 years’ software engineering or related experience.
Large-scale machine learning workloads on GPUs.
Nvidia DeepStream pipeline configuration, plugin authoring, multi-stream multiplexing, and production debugging.
Nvidia Triton Inference Server, including model repositories, ensemble pipelines, dynamic batching, and backend configuration.
Production deployment and scaling of PyTorch models, TorchScript, TensorRT, FP16/INT8 tuning, and model versioning.
GStreamer element graph design, message handling, pad linking, and latency profiling.
Python proficiency, including scripting, automation, multiprocessing, NumPy, and Pandas.
Kubernetes operations, GPU scheduling, Helm charts, canary rollouts, and automated rollback.
ML observability, metrics, monitoring, alerting, dashboarding, drift detection, and data-quality detection.
Git, MLOps, CI/CD, networking, cloud technologies such as GCP or Azure, RDBMS, and NoSQL/caching solutions.
Object detection, object segmentation, tracking models, and GenAI deployment patterns.
Testing frameworks, E2E and regression testing, pre-deployment validation, and shadow testing.
Authentication, authorization, permissions, and privacy best practices.
Basic REST API development using Node.js and Spring Boot.
JavaScript, TypeScript, React, React Native, HTML, and CSS/SASS.
Comfortable with ambiguity and a self-motivated learner and builder.

Benefits

Comp & perks

Incentive awards for performance
Maternity and parental leave
PTO
Health benefits
Flexible arrangements to manage personal lives