MS or PhD in Computer Science, Machine Learning, or related technical field, or equivalent industry experience. 15+ years of experience building large-scale ML or distributed systems, with a track record of technical leadership and industry-wide or company-wide impact. Deep, hands-on expertise in foundation model inference engines, with a proven record of improving performance, efficiency, and reliability at scale. Deep experience supporting a diverse set of foundation model inference use cases, each with different throughput, latency, cost, and quality constraints.
Breadth beyond inference – the ability to contribute in adjacent systems areas such as training infrastructure, data systems, or evaluation. Deep understanding of GPU/TPU/accelerator architecture, distributed systems, and model optimization (quantization, distillation, compilation, serving). Proficiency with ML frameworks such as JAX, PyTorch, and with inference/serving stacks. Proven experience leading a diverse set of engineers in setting vision and driving execution, including prioritization for milestone deliveries.
Demonstrated experience mentoring junior and senior engineers. Demonstrated experience partnering with ML researchers and modeling teams to productionize research.