Full-Time

Principal Engineer

Distributed Machine Learning

Posted on 1/7/2025

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI computing solutions

Automotive & Transportation
Enterprise Software
AI & Machine Learning
Gaming

Compensation Overview

$272k - $425.5kAnnually

+ Equity

Expert

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

Category
Applied Machine Learning
Deep Learning
AI & Machine Learning
Software Engineering
Required Skills
Kubernetes
Python
Pytorch
Apache Spark
Scala
C/C++
Yarn

You match the following NVIDIA's candidate preferences

Employers are more likely to interview you if you match these preferences:

Degree
Experience
Requirements
  • 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
Responsibilities
  • 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 spark-rapids-ml 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, spark-rapids-ml, 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
Desired Qualifications
  • Familiarity with NVIDIA libraries (RAPIDS cuML, Spark-RAPIDS, NVTabular) 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

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC). These products help developers, data scientists, and IT administrators perform complex tasks efficiently. NVIDIA differentiates itself from competitors by focusing on technological advancements and a diverse range of applications, including cloud-based services like NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. The company's goal is to drive innovation in computing and provide effective solutions for a wide array of clients, from gamers to enterprises.

Company Stage

IPO

Total Funding

$19.5M

Headquarters

Santa Clara, California

Founded

1993

Growth & Insights
Headcount

6 month growth

0%

1 year growth

0%

2 year growth

-1%
Simplify Jobs

Simplify's Take

What believers are saying

  • Acquisition of VinBrain enhances NVIDIA's AI-driven healthcare solutions.
  • Investment in Nebius Group boosts NVIDIA's AI infrastructure capabilities.
  • Partnership with Serve Robotics aligns with NVIDIA's focus on robotics and AI applications.

What critics are saying

  • Increased competition from AI startups like xAI challenges NVIDIA's market position.
  • Serve Robotics' rapid expansion may lead to financial strain if market growth lags.
  • Integration challenges from VinBrain acquisition may affect NVIDIA's operational efficiency.

What makes NVIDIA unique

  • NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.
  • The Omniverse platform enhances NVIDIA's capabilities in industrial AI and digital twins.
  • NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

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Benefits

Company Equity

401(k) Company Match

INACTIVE