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
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.
