Software Engineer – Data and Observability Platform

NVIDIA•Austin, TX

About The Position

NVIDIA’s Hardware Infrastructure organization seeks a high-caliber Software Engineer to join the Data & Observability Platform team. We provide the data backbone for NVIDIA’s GPU/CPU development and AI research teams. Our platform manages large-scale telemetry that supports observability, reliability analysis, and capacity forecasting for EDA datacenters. We are looking for a software engineer with excellent coding skills and full-stack experience. You should be proficient in software engineering principles and experienced with distributed systems/data-intensive infrastructure. At NVIDIA, you will build and maintain high-throughput services that collect and process metrics, logs, and hardware telemetry from compute clusters. You will design and deploy internal web applications, tools, and APIs to configure data pipelines, visualize platform health, and integrate AI workflows. You will contribute to modernizing the Data Platform, ensuring it aligns with industry trends and supports our unique needs and use cases. You will manage the deployment and lifecycle of services on Kubernetes and Cloud. You will partner with users to refactor data schemas, identify latency bottlenecks, and apply storage patterns to reduce operational costs. You will ensure high platform availability through detailed on-call practices and proactive monitoring of complex data dependencies. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

Requirements

BS or MS in Computer Science, Electrical Engineering, or a related technical field or equivalent experience.
2+ years of experience writing production-grade code.
High proficiency in Python or Java/Scala, with an emphasis on writing testable, performant, and asynchronous code.
A deep understanding of Distributed Systems and Linux internals.
Experience building end-to-end features from the ground up, including backend APIs development and schemas to implementing functional frontends.
Proficiency in SQL and an understanding of data modeling, read/write tradeoffs, query execution patterns, and optimized data formats.
Comfortable debugging production issues, distributed traces, and memory leaks in a high-scale environment.
Ability to break down ambiguous problems into simpler, executable components in a fast-paced environment.

Nice To Haves

Experience with frameworks/tools like Spark, Kafka, Trino.
Familiarity with partition strategies, offset management, and backpressure.
Hands-on experience with Kubernetes, service deployments, Infrastructure as Code (Terraform), and cloud administration.
Familiarity with Prometheus, Grafana, or OpenSearch, and the ability to build custom exporters or telemetry dashboards.
Experience with HPC environments, semiconductor build workflows, or handling large-scale hardware telemetry.

Responsibilities

Build and maintain high-throughput services that collect and process metrics, logs, and hardware telemetry from compute clusters.
Design and deploy internal web applications, tools, and APIs to configure data pipelines, visualize platform health, and integrate AI workflows.
Contribute to modernizing the Data Platform, ensuring it aligns with industry trends and supports our unique needs and use cases.
Manage the deployment and lifecycle of services on Kubernetes and Cloud.
Partner with users to refactor data schemas, identify latency bottlenecks, and apply storage patterns to reduce operational costs.
Ensure high platform availability through detailed on-call practices and proactive monitoring of complex data dependencies.