Principal Systems Software Engineer
About The Position
NVIDIA is seeking a Sr. Principal Systems Software Engineer for the Apache Spark Acceleration group. GPU accelerated data processing has moved from proof of concept to production deployments. Enterprises now recognize the need for accelerated computing to handle large data processing needs. Multi-node GPU deployments will reduce cloud computing costs and lower latency batch ETL workloads. At NVIDIA, we have been invested in accelerating Apache Spark, providing an open source plugin for Apache Spark. Apache Spark is the most popular data processing engine in data centers. We strive to accelerate Spark applications on GPUs without any code changes. Our OSS RAPIDS Spark library is integrated with on-premise and cloud services such as AWS EMR, Databricks, Google Dataproc, Oracle Cloud Data Flow, Bytedance Volcengine, Tencent Cloud and Cloudera. We are passionate about working on hard problems that have an impact. You will need to have strong programming skills, a deep understanding of software development related. You will work with a team that is using open source libraries like cuDF to accelerate reading, writing and batch data operations in Spark.
Requirements
- BS, MS, or PhD in Computer Science, Computer Engineering, or closely related field (or equivalent experience)
- 15+ years of work experience in software development
- Outstanding technical skills in designing and implementing high-quality distributed systems
- Excellent programming skills in C++, Java, and/or Scala
- Ability to work with teams across organizational boundaries and geographies
- Familiarity with the open source data platform ecosystem (Apache Spark, Velox, Presto, Apache Arrow, Apache DataFusion, etc.).
- Highly motivated with strong interpersonal skills
Nice To Haves
- Meaningful contributions to the OSS community a plus.
- Database query optimization is a strong plus
Responsibilities
- Develop Java, Scala and CUDA/C++ libraries to accelerate DataFrames and I/O operations on common file formats such as Parquet, ORC and JSON
- Enable interoperability with table formats such as Apache Iceberg and Delta Lake, and metastores such as Unity Catalog
- Work with open source communities to enhance libraries like NVIDIA cuDF, CCCL and UCX through technical discussion and code contributions
- Collaborate with distributed systems teams to craft solutions to distributed processing problems challenges at large scale
- Provide recommendations and feedback to teams regarding decisions surrounding topics such as infrastructure, continuous integration and testing strategy
- Build, test and optimize CUDA/C++ libraries across different platforms
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
