Senior Software Engineer, GNN

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Create accelerated solutions using PyTorch for large-scale machine learning models, including GNNs, TFMs, and ensemble models, emphasizing efficient training and inference on GPU infrastructure.

• Assist with CUDA-X Libraries and integrations utilized in PyTorch-based, large-scale machine learning workflows.

• Collaborate with developers, product managers, and scientists to design innovative GNN models and GPU-accelerated implementations for both model development and prediction stages.

• Develop solutions that facilitate customer adoption of NVIDIA hardware and software, while collecting technical requirements directly from customers and Solutions Architects to inform product and engineering priorities.

• Offer technical leadership and mentorship to engineers within the team.

• Identify opportunities for codebase enhancements and reduce maintenance overhead through re-architecture.

• Utilize agentic coding tools to detect and resolve bugs, implement new features, and refactor existing code.

• Address complex technical challenges, clearly articulate solutions, demonstrate technical leadership, and coordinate efforts across multiple teams to achieve common goals.

⛳️ Requirements

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

• A minimum of 3 years of experience working with PyTorch.

• At least 2 years of experience in training enterprise-scale machine learning models across distributed infrastructure.

• Over 2 years of experience in designing and managing efficient training and inference workflows on GPU infrastructure, including aspects like profiling, scaling, orchestration, and resource utilization.

• Exceptional C++ programming and software design capabilities.

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

• Strong collaboration, communication, and documentation practices.

• Experience in developing or deploying Graph Neural Network solutions with PyTorch Geometric or a comparable framework.

• Familiarity with data warehouse and lakehouse platforms such as Snowflake or Databricks.

• Background in two or more of the following sectors: finance, cybersecurity, government or national laboratories, and retail.

• Comprehensive understanding of system architecture, including CPU, GPU, memory, and storage systems, as well as performance optimization techniques.

• Experience in customer engagement and technical support, especially concerning data science workflows and vector search and storage solutions like FAISS or Milvus.

🏝️ Benefits

• Equity

• Benefits