Member of Technical Staff (Performance Optimization)

Join our team as a Member of Technical Staff focused on Performance Optimization. In this role, you will optimize performance at every layer of the stack, from low-level GPU kernels to large-scale distributed systems. You will work closely with teams across research, infrastructure, and systems to identify performance bottlenecks, implement optimizations, and scale our AI systems. Your work will directly impact the speed, scalability, and cost-effectiveness of advanced generative AI models.

• Optimizing performance at every layer of the stack, from low-level GPU kernels to large-scale distributed systems.
• Collaborating with teams across research, infrastructure, and systems to identify performance bottlenecks and implement optimizations.
• Building and maintaining performance benchmarking and monitoring infrastructure, and scaling inference and training systems across multi-GPU, multi-node environments.

• Experience with distributed system debugging and optimization in multi-GPU environments
• Deep understanding of GPU architecture, parallel programming models, and compute kernels
• Proficiency in CUDA or ROCm and experience with GPU profiling tools (e.g., Nsight, nvprof, CUPTI)
• Familiarity with PyTorch and performance-critical model execution
• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
• 5+ years of experience working on performance optimization or high-performance computing systems
• Experience optimizing large models for training and inference (LLMs, VLMs, or video models)
• Master’s or PhD in Computer Science, Electrical Engineering, or a related field
• Contributions to open-source ML or HPC infrastructure
• Knowledge of compiler stacks or ML compilers (e.g., torch.compile, Triton, XLA)
• Familiarity with cloud-scale AI infrastructure and orchestration tools (e.g., Kubernetes)
• Background in ML systems engineering or hardware-aware model design