Principal Systems Software Engineer

Posted 2 Days Ago
Be an Early Applicant
Santa Clara, CA
Expert/Leader
Artificial Intelligence • Hardware • Robotics • Software • Metaverse
The Role
The Principal Systems Software Engineer will lead the design and implementation of accelerated Apache Spark and big-data frameworks, develop libraries for data analytics and machine learning, engage with open source communities, and collaborate with partners to deploy advanced solutions. They must have extensive experience with key big data projects and strong programming skills in C++, Java, and Scala.
Summary Generated by Built In

We are seeking expert System Software Engineers to join our Apache Spark Acceleration team. Data scientists spend a considerable amount of time exploring data and iterating over machine learning (ML) experiments. NVIDIA believes that data science and analytics workflows can benefit tremendously from being accelerated, to enable data users to explore more and larger datasets to drive towards their business goals faster and more optimally.

You will work with the open source community to accelerate Apache Spark with GPUs for data science. Apache Spark is the most popular data processing engine in data centers. We strive to significantly accelerate Apache Spark 3.x use cases without application code changes. You will work on open source libraries (such as https://nvidia.github.io/spark-rapids/) to be used in both on-premises and cloud services (such as Databricks, AWS EMR, Google Dataproc, and Cloudera).

What you'll be doing:

  • Leading the design and implementation of accelerated Apache Spark and related big-data frameworks

  • Creating a collection of accelerated libraries for data analytics and machine learning

  • Working with a team of outstanding engineers including PMC and Committers of Apache Spark, Apache Hadoop, Apache Hive, and Apache Arrow

  • Engaging open source communities (including Apache Spark, RAPIDS and UCX) for technical discussion and contribution

  • Working with NVIDIA strategic partners on deploying advanced machine learning and data analytics solutions in public cloud or on-premise clusters

  • Presenting technical solutions in industry conferences and meetups

  • 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

What we need to see:

  • BS, MS, or PhD in Computer Science, Computer Engineering, or closely related field or equivalent experience

  • 15+ years of work experience in software development

  • 5+ years working experience with key open source big-data projects as a contributor or committer including Apache Spark, Apache Flink, Trino, Apache Kafka, Apache Hive, Apache Arrow, Apache Hadoop, Delta Lake, Apache Iceberg

  • Outstanding technical skills in designing and implementing high-quality distributed systems

  • Excellent programming skills in C++, Java, and/or Scala

  • Ability to work successfully with multi-functional teams across organizational boundaries and geographies

  • Highly motivated with strong interpersonal skills

The base salary range is 272,000 USD - 419,750 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.

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.

Top Skills

C++
Java
Scala
The Company
HQ: Santa Clara, CA
21,960 Employees
On-site Workplace
Year Founded: 1993

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.”

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