You will work together on a collaborative team of researchers and engineers with diverse backgrounds and technical training. Your goal will be to make our customers successful in applying state-of-the-art LLMs and AI systems, and we encode our scientific expertise into our products to make that possible.

Your responsibilities will include:

• Driving performance improvements through advanced optimization techniques including kernel fusion, mixed precision, memory layout optimization, tiling strategies, and tensorization for training-specific patterns

• Designing, implementing, and optimizing high-performance GPU kernels for training workloads (e.g., attention mechanisms, custom layers, gradient computation, activation functions) targeting NVIDIA architectures

• Designing and implementing distributed training frameworks for large language models, including parallelism strategies (data, tensor, pipeline, ZeRO-based) and optimized communication patterns for gradient synchronization and collective operations

• Profiling, debugging, and optimizing end-to-end training workflows to identify and resolve performance bottlenecks, applying memory optimization techniques like activation checkpointing, gradient sharding, and mixed precision training

We look for candidates with a strong background in computer science or a related field, hands-on experience writing and tuning CUDA kernels for ML training applications, and a deep understanding of parallelism techniques and memory optimization strategies for large-scale model training.

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