AI Performance Engineer (Cloud AI Engineering), Sr | Staff | Sr. Staff

Convert, optimize and deploy models for efficient inference using PyTorch, ONNX. Work at the forefront of GenAI by understanding advanced algorithms (e.g. attention mechanisms, MoEs) and numerics to identify new optimization opportunities. Performance analysis and optimization of LLM, VLM, and diffusion models for inference. Scale performance for throughput and latency constraints. Mapping the next generation AI workloads on top of current and future hardware designs. Work closely with customers to drive solutions by collaborating with internal compiler, firmware and platform teams. Analyze complex performance or stability issues to work towards final root cause of underlying problems. Create engineering solutions to deliver continuous insights into performance of AI workloads guiding the improvements over time. Design and implement high-level kernels, e.g. in Triton, with a focus on generating efficient, low-level code. Experience in workload mapping strategies exhibiting sharding or various parallelisms. Background in neural network operators and mathematical operations, including linear algebra and math libraries. Understanding of machine learning compilers. Experience in converging accuracy and its evaluation methods. Knowledge of torch.compile or torchDynamo. PhD in Computer Science, Computer Engineering or Machine Learning Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 5+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.