Senior Systems Software Engineer - Accelerated Spark Applications

NVIDIA•Champaign, IL

About The Position

NVIDIA is seeking a Senior Systems Software Engineer for its Apache Spark Acceleration group. The team accelerates Apache Spark applications on GPUs through an open-source plugin and cuDF Spark library. This role supports an internal hosted Spark service that runs production data pipelines for NVIDIA’s internal data platform, processing thousands of jobs per hour on Kubernetes and AWS under a customer SLA. The service packages the cuDF Spark plugin and a CPU/GPU workload router into an API-based service built on Spark Connect, Kyuubi, Helm, and Flux. The engineer will work across the service, GPU Spark runtime, and workload router.

Requirements

Bachelor’s, master’s, or PhD in Computer Science, Computer Engineering, or a closely related field, or equivalent experience.
At least 9 years of software development experience.
Strong distributed-systems fundamentals.
Proficiency in Java or Scala, plus C++ or Python.
Hands-on experience operating services on Kubernetes, on-premises or in the cloud.
Experience owning or reviewing a long-lived server, such as REST front ends, proxies or request routers, or JDBC or Thrift services.
Experience supporting enterprise or internal production customers, including on-call experience.
Experience using AI tools in software development.

Nice To Haves

Apache Spark internals, Spark Connect, or Kyuubi experience.
Delta Lake, Apache Iceberg, or Unity Catalog integration work.
GPU data processing with cuDF or CUDA experience.
Significant contributions to collaborative, shared-development data platform projects such as Apache Spark, Velox, Presto, Apache Arrow, or Apache DataFusion.

Responsibilities

Design, build, and operate the hosted Spark service control plane on Kubernetes, including the Java REST API, Spark Connect runner, Kyuubi, and deployment using Helm charts and Flux.
Own code review for one or more services end to end and keep them releasable on the release train.
Extend the cuDF Spark runtime and CPU/GPU workload router in Scala and Java, including interoperability with Delta Lake, Apache Iceberg, and Unity Catalog.
Investigate production failures on clusters running more than a thousand concurrent jobs with customer and upstream library teams, and drive fixes to closure.
Instrument metrics, dashboards, and alerts; participate in the on-call rotation and keep runbooks current.
Provide recommendations and feedback to teams on infrastructure, continuous integration, and testing strategy.