Full-Time

Senior Research Engineer

Foundation Model Training Infrastructure

Confirmed live in the last 24 hours

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI computing solutions

Automotive & Transportation
Enterprise Software
AI & Machine Learning
Gaming

Compensation Overview

$224k - $356.5kAnnually

+ Equity

Expert

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

Category
Applied Machine Learning
Robotics & Autonomous Systems
Deep Learning
AI & Machine Learning
Required Skills
Kubernetes
Python
Tensorflow
CUDA
Pytorch
C/C++

You match the following NVIDIA's candidate preferences

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

Degree
Experience
Requirements
  • Bachelor's degree in Computer Science, Robotics, Engineering, or a related field;
  • 10+ years of full-time industry experience in large-scale MLOps and AI infrastructure;
  • Proven experience designing and optimizing distributed training systems with frameworks like PyTorch, JAX, or TensorFlow.
  • Deep understanding of GPU acceleration, CUDA programming, and cluster management tools like Kubernetes.
  • Strong programming skills in Python and a high-performance language such as C++ for efficient system development.
  • Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools (e.g., SLURM, Kubernetes).
Responsibilities
  • Design and maintain large-scale distributed training systems to support multi-modal foundation models for robotics.
  • Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
  • Implement scalable data loaders and preprocessors tailored for multimodal datasets, including videos, text, and sensor data.
  • Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows on large GPU clusters.
  • Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines.
Desired Qualifications
  • Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field;
  • Demonstrated Tech Lead experience, coordinating a team of engineers and driving projects from conception to deployment;
  • Strong experience at building large-scale LLM and multimodal LLM training infrastructure;
  • Contributions to popular open-source AI frameworks or research publications in top-tier AI conferences, such as NeurIPS, ICRA, ICLR, CoRL.

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) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through hardware sales, software licenses, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance applications in AI, machine learning, and computer vision. Unlike many competitors, NVIDIA focuses heavily on research and development to maintain its leadership in technology and innovation. The company's goal is to drive advancements in AI and computing to provide effective solutions for a wide range 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