STAFF, SOFTWARE ENGINEER (Machine Learning)

Position Summary… What youll do…

Skill: Machine Learning Engineer Exp range: 8 - 13 years

About Team:

This position is in the data science engineering team called Global Governance under the Corporate Technology Services organisation. Enterprise areas of Walmart like Finance, Indirect Procurement, People Tech and others are in the midst of massive digital transformation.

Our team is building products for these areas from scratch, dealing with challenges from gathering user data, developing machine learning models to understand business processes better and making them more holistically data driven and intelligent. Video Analytics, Agentic Workflows, Object Detection and Segmentation, Optimisation, Anomaly Detection are only some of the areas we are actively working on. We leverage cutting edge deep learning and computer vision, GenAI technologies to solve business critical problems.

Do you enjoy developing, deploying and handling complex production environments and collaborating with cross-functional teams to maintain optimal application performance As a Staff Software Engineer , you are responsible for translating requirements into technical solutions with the focus of creating the best user experience for our associates and customers. You will be part of a project team consisting of other data scientists and engineers who will work together to deliver deploy ML and GenAI models at scale, business value and ensure code quality. Communication is also key as you will need to work closely with product owners, data scientists engineers.

What youll do:

As a Staff Software Engineer ( MLE ) for Walmart, you ll have the opportunity to:

• Build and maintain low-latency/high-throughput Computer Vision (real-time/batch) pipelines at Walmart scale and complexity.

• Build scalable and reusable components and productionize the solution by deploying end-to-end pipelines for ML GenAI solutions.

• Drive data-derived insights across a wide range of divisions by supporting statistical models, machine learning algorithms, and computational algorithms based on business initiatives.

• Direct the gathering of data, assessing data validity, and synthesizing data into large analytics datasets to support project goals.

• Communicate recommendations to business partners and influence future plans based on insights.

• Take ownership of achieving complete automation with design for the Vision solution, with a good level of comfort with shell, API design, queuing, and advanced SQL/NoSQL concepts.

• Utilize big data analytics and advanced MLOps data science techniques to identify trends, patterns, and discrepancies in data.

• Enhance the performance of applications while identifying and addressing bottlenecks from the standpoint of both web applications and machine learning models.

• Apply your computer vision and forecasting expertise where required .

What you ll bring:

• Good understanding of running large-scale ML workloads on GPUs .

• Strong hands-on experience with Nvidia DeepStream - pipeline configuration, plugin authoring, multi-stream multiplexing, and production-level debugging of inference graphs.

• Strong hands-on experience with Nvidia Triton Inference Server - model repository layout, ensemble pipelines, dynamic batching, and TensorRT / PyTorch /ONNX b ackend configuration.

• Experience deploying and scaling PyTorch models in production - TorchScript , TensorRT export, FP16/INT8 precision tuning, and model versioning.

• Good expertise in GStreamer - element graph design, message handling, pad linking, and end-to-end pipeline latency profiling from source to downstream integration(s).

• Good to have working knowledge of C++ for CUDA and DeepStream / GStreamer plugin development

• Good hands-on experience building ML observability stacks for complex ML and GenAI pipelines - metrics, monitoring, alerting, and dashboarding (e.g., Grafana/Prometheus with PromQL , latency-SLO instrumentation; log aggregation via Splunk), drift and model/data-quality detection.

• Strong Kubernetes operations skills - pod lifecycle management, GPU scheduling, custom Helm charts, and safe-deployment patterns such as canary rollouts and automated rol…