Distinguished Engineer, Apache Spark

Distinguished Engineer - Accelerated Apache Spark

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

What we need to see :

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

Ways to stand out from the crowd :

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

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 308,000 USD - 471,500 USD.

You will also be eligible for equity and benefits () .

Applications for this job will be accepted at least until August 5, 2025.

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.

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