NVIDIA is seeking a Distinguished Engineer for the Apache Spark Acceleration group.
Over the past five years GPU accelerated data processing has moved from proof of concept to production deployments. Many enterprises are now recognizing the needs of accelerated computing for large scale data processing. Multi-node GPU deployments will reduce cloud computing costs and lower latency of large scale data processing.
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.
You will serve as a hands-on architect of Nvidia Spark Acceleration Group. You will work with a team of distributed system engineers including PMC and Committers of Apache Spark, Apache Hadoop, Apache Hive, and Apache Arrow. You will engage in open source projects such as Apache Spark, RAPIDS, Apache Iceberg, Delta Lake, UCX and more.
What you'll be doing:- Lead the architecture, design and implementation of accelerated Apache Spark and related big-data frameworks
- Engage open source communities (including Apache Spark, RAPIDS, Apache Iceberg, Delta Lake and UCX) for technical discussion and contribution, and engage new communities where we may not have a strong presence yet
- Work with NVIDIA partners to deploy GPU enabled data analytics solutions in public cloud or on-premises clusters
- Present technical solutions at industry conferences and meetups
- Collaborate with distributed systems teams to define 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
- Build automation and tools that will increase the productivity of teams developing distributed systems
- Mentor members of the engineering team
- BS, MS, or PhD in Computer Science, Computer Engineering, or closely related field (or equivalent experience)
- 17+ years of work or research experience in software development
- Prior experience in delivering complex software projects as a lead architect
- Outstanding technical skills in designing and implementing high-quality distributed systems
Excellent programming skills in C++, Java, and/or Scala
- Highly motivated with strong interpersonal skills and communication skills
- 5+ years working experience with key open source big-data projects as a contributor or committer to Apache Spark, Apache Hadoop, Apache Flink, Apache Kafka, Apache Hive, Apache Arrow, Delta Lake
- Excellent knowledge about distributed system schedulers: Kubernetes, Hadoop YARN, Apache Spark
- Able to delve into a new area and quickly come up to speed
Able to work with teams across boundaries and geographies
- Working experience in designing and developing columnar query engines would be a huge plus
- Committership at major open source projects (such as Apache Spark, Apache Hadoop, Apache Flink) is a big plus
- Working experience with acceleration libraries (CUDA, RAPIDS, UCX) is helpful
You will also be eligible for equity and benefits.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
