About the Team
We are dedicated to building the inference infrastructure for ultra-large-scale language models, vision-language models, and frontier multimodal AI systems.
Our mission is to provide a robust, scalable, and high-performance foundation for distributed serving, heterogeneous scheduling, and low-latency inference at massive scale.
You will work on some of the most challenging problems in large-model online serving, spanning traffic orchestration, throughput and latency optimization, kernel efficiency, and production reliability for next-generation AI systems.
We are looking for talented individuals to join our team.
As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year.
Please state your availability and graduation date clearly in your resume.
Responsibilities - What You'II Do - Build and evolve next-generation inference systems for large-scale online traffic, including global scheduling across heterogeneous compute resources, high-concurrency load balancing, and efficient batch formation - Optimize distributed inference for 200B+ models and complex multimodal models through TP, EP, DP, and related strategies to improve throughput and latency in production - Develop high-performance kernels for frontier model architectures such as MoE, emerging attention mechanisms, and multimodal fusion layers using CUDA, Triton, and related tools - Explore AI-driven infrastructure for inference systems, including AI Agents for kernel optimization, performance tuning, consistency validation, deployment pipelines, and intelligent operations