Principal Engineer, Distributed Machine Learning Specialist

16 hours ago

Santa Clara, California, United States NVIDIA Full time

NVIDIA is seeking a highly skilled Principal Engineer to join our Distributed Machine Learning team. As a key member of our team, you will be responsible for designing and developing innovative solutions to accelerate and scale machine learning workloads on GPU-enabled Spark clusters.

Key Responsibilities:

  • Design and develop new user-friendly APIs and libraries to optimize the use of existing deep learning and machine learning frameworks in GPU-enabled Spark clusters.
  • Develop GPU-accelerated machine learning libraries for distributed training and inference on Spark clusters, focusing on improving performance and usability.
  • Demonstrate the superiority of developed solutions on industry-standard benchmarks and datasets.
  • Contribute to the enhancement of open-source projects such as RAPIDS, XGBoost, and Apache Spark.
  • Collaborate with NVIDIA partners and customers to deploy distributed machine learning algorithms in cloud or on-premise environments.
  • Stay up-to-date with published advances in distributed machine learning systems and algorithms.
  • Provide technical mentorship to a team of engineers.

Requirements:

  • BS, MS, or PhD in Computer Science, Computer Engineering, or a closely related field (or equivalent experience).
  • 12+ years of work or research experience in software development.
  • 5+ years of experience as a 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 the 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.
  • Familiarity with agile software development practices.

Preferred Qualifications:

  • Familiarity with NVIDIA libraries, such as RAPIDS cuML, Spark-RAPIDS, and NVTabular.
  • Familiarity with NVIDIA GPUs and CUDA.
  • Familiarity with Horovod, Petastorm, and other existing/past distributed learning libraries.
  • Experience working with multi-functional teams across organizational boundaries and geographies.

NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer. We do not discriminate 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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