Senior Software Engineer, GPU Local AI Platforms

What you'll do

• Track and evaluate innovations in open-source LLM inference frameworks to identify performance-critical features for NVIDIA edge hardware.
• Analyze how new model architectures and inference algorithms map onto NVIDIA GPU architecture to identify optimization opportunities.
• Characterize multi-node inference behavior including collective communication primitives and parallelism efficiency on edge cluster configurations.
• Produce performance analysis reports mapping theoretical hardware limits to observed inference throughput, latency, and utilization.
• Own the model validation workflow for new releases, including architecture compatibility assessment and performance characterization.
• Develop and maintain developer-facing inference recipes while automating staleness detection and building feedback loops from CI results.
• Serve as the technical point of contact for community and partner questions regarding hardware-specific inference issues.

What we're looking for

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
• 12+ years of software engineering experience with depth in GPU computing, ML systems, or high-performance inference.
• Strong Python or C++ programming, software design, and software engineering skills.
• Hands-on experience with GPU kernel development or optimization using CUDA/C++, Triton, or equivalent.
• Working knowledge of LLM inference internals, including attention mechanisms, KV-cache management, quantization, and tensor parallelism.
• Container engineering expertise including multi-architecture Docker or OCI builds, layer optimization, and NVIDIA Container Toolkit.
• Strong analytical skills to form performance hypotheses, design experiments, interpret results, and communicate findings clearly.