Nvidia Principal Engineer Distributed Machine Learning 

United States, California 

335430263

What you’ll be doing:

• Design and develop new user-friendly APIs and libraries to optimally use existing DL/ML frameworks in GPU-enabled Spark clusters for distributed DL/ML training and inference at scale.

• Design and develop GPU accelerated ML libraries for distributed training and inference on Spark clusters, e.g., to improve our existing open source library.

• Demonstrate superior performance of developed solutions on industry standard benchmarks and datasets.

• Make technical contributions to enhance capabilities of open source projects such as RAPIDS, XGBoost, , and Apache Spark.

• Work with NVIDIA partners and customers on deploying distributed ML algorithms in cloud or on-premise.

• Keep up with published advances in distributed ML systems and algorithms.

• Provide technical mentorship to a team of engineers.

What we need to see:

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

• 12+ years of work or research experience in software development.

• 5+ experience as technical lead in distributed machine learning and/or deep learning.

• 3+ years of open source development experience.

• 3+ years of hands-on experience with Spark MLlib, XGBoost, and/or PyTorch.

• Knowledge of internals of Apache Spark MLlib.

• Experience with Kubernetes, YARN, Spark, and/or Ray for distributed ML orchestration.

• Proven technical skills in designing, implementing and delivering high-quality distributed systems.

• Excellent programming skills in C++, Scala, and Python.

• Familiar with agile software development practice.

Ways to stand out from the crowd:

• Familiarity with NVIDIA libraries ( , ) is a plus.

• Familiarity with NVIDIA GPUs and CUDA is also a strong plus.

• Familiarity with Horovod, Petastorm and other existing/past distributed learning libraries is desirable.

• Experience working with multi-functional teams across organizational boundaries and geographies.