Senior Software Engineer, GNN

Posted 25 Days Ago
3 Locations
In-Office or Remote
152K-288K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop GPU-accelerated, PyTorch-based machine learning solutions, including Graph Neural Networks, foundation models, and ensemble models. Build and optimize distributed training and inference workflows, support CUDA-X integrations, improve high-performance code, collaborate with customers and cross-functional teams, and provide technical leadership and mentorship.
Summary Generated by Built In

NVIDIA is seeking a highly experienced and passionate Senior Software Engineer to join a team building large-scale machine learning solutions, including Graph Neural Networks, Tabular Foundation Models, and ensemble models. This role is critical to accelerating PyTorch-based frameworks and supporting user-facing tools that power NVIDIA’s cutting-edge data science solutions. The team works at the intersection of high-performance computing, GPU acceleration, machine learning infrastructure, and customer-facing software. This is an opportunity to shape efficient training and inference workflows for advanced machine learning models running on NVIDIA GPU infrastructure. If you are passionate about building high-performance software and enabling customers to solve complex data science problems at scale, we would love to hear from you. NVIDIA teams work on some of the world’s most ambitious computing problems, helping organizations adopt AI technologies that enable faster, smarter decisions.
 

What you’ll be doing
  • Develop accelerated, PyTorch-based solutions for large-scale machine learning models, including GNNs, TFMs, and ensemble models, with a focus on efficient training and inference on GPU infrastructure

  • Support CUDA-X Libraries and integrations used in PyTorch-based, large-scale machine learning workflows

  • Partner with developers, product managers, and scientists to develop innovative GNN models and GPU-accelerated implementations for model development and prediction phases

  • Develop solutions that help customers adopt NVIDIA hardware and software, and gather technical requirements directly from customers and Solutions Architects to guide product and engineering priorities

  • Provide technical leadership and mentorship to engineers across the team

  • Identify opportunities to improve the codebase and reduce code-maintenance overhead through re-architecture

  • Apply agentic coding tools to identify and fix bugs, implement new features, and refactor code

  • Solve complex technical issues, explain solutions clearly, exercise technical leadership, and coordinate across multiple teams to achieve shared objectives

What we need to see:

  • Bachelor’s degree (or equivalent experience) plus 5 or more years of relevant experience in large-scale machine learning, deep learning, and general data science; or a Master’s degree or PhD plus 3 or more years of relevant experience

  • 3 or more years of experience with PyTorch

  • 2 or more years of experience training enterprise-scale machine learning models across distributed infrastructure

  • 2 or more years of experience designing and operating efficient training and inference workflows on GPU infrastructure, including profiling, scaling, orchestration, and resource utilization

  • Excellent C++ programming and software design skills

  • Proven experience developing, debugging, and optimizing high-performance applications, preferably with GPU acceleration using CUDA

  • Strong collaboration, communication, and documentation habits

Ways to stand out from the crowd:

  • Experience developing or deploying Graph Neural Network solutions using PyTorch Geometric, or a similar framework

  • Experience working with data warehouse and lakehouse platforms, such as Snowflake or Databricks

  • Experience in two or more of the following domains: finance, cybersecurity, government or national laboratories, and retail

  • Strong understanding of system architecture, CPU, GPU, memory, and storage systems, as well as performance optimization

  • Experience with customer engagement and technical support, particularly for data science workflows and with vector search and storage solutions, such as FAISS or Milvus
     

NVIDIA is widely considered one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us. If you are creative and autonomous, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 19, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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.

Skills Required

  • Bachelor's degree or equivalent experience plus 5 or more years of relevant experience, or a Master's degree or PhD plus 3 or more years of relevant experience
  • Experience in large-scale machine learning, deep learning, and general data science
  • 3 or more years of experience with PyTorch
  • 2 or more years of experience training enterprise-scale machine learning models across distributed infrastructure
  • 2 or more years of experience designing and operating efficient GPU training and inference workflows, including profiling, scaling, orchestration, and resource utilization
  • Excellent C++ programming and software design skills
  • Experience developing, debugging, and optimizing high-performance applications
  • Strong collaboration, communication, and documentation skills
  • GPU acceleration experience using CUDA
  • Experience developing or deploying Graph Neural Networks using PyTorch Geometric or a similar framework
  • Experience with Snowflake or Databricks data warehouse and lakehouse platforms
  • Experience in finance, cybersecurity, government or national laboratories, or retail
  • Understanding of system architecture, CPU, GPU, memory, storage systems, and performance optimization
  • Customer engagement and technical support experience for data science workflows
  • Experience with vector search and storage solutions such as FAISS or Milvus

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
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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