Senior Software Engineer, CUDA Deep Learning Systems

What you'll do

• Prototype novel systems optimizations for deep learning models using modeling, simulation, and silicon prototyping.
• Architect and optimize distributed computing systems that scale from single nodes to cluster-scale supercomputing environments.
• Design, implement, and optimize custom high-performance CUDA kernels for emerging neural network architectures.
• Analyze hardware-software interactions to identify and resolve performance bottlenecks in training and inference pipelines.
• Develop exploratory tools and runtime systems to profile and accelerate new deep learning paradigms.
• Write maintainable code to transition exploratory prototypes into open-source releases or commercial products.
• Co-design systems and algorithms to improve accelerator compute utilization, memory bandwidth, and network communication efficiency.

What we're looking for

• BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field (or equivalent experience).
• 8+ years of relevant industry experience or equivalent academic experience after degree achievement.
• Strong proficiency in C++ and Python programming.
• Solid background in deep learning fundamentals with a focus on transformers.
• Strong understanding of distributed computing principles, multi-node scaling, and cluster-scale execution performance challenges.
• Proven experience in systems programming, computer architecture, and low-level systems performance optimization.
• Familiarity with deep learning accelerator architectures and hands-on experience with CUDA programming, kernel optimization, and workload profiling.
• Experience profiling and optimizing generative AI models, including large language models, vision models, or diffusion models.