Research Engineer - LLM/VLM Inference Optimization (Seed Infra)

Job description

About the Team The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models. Responsibilities: 1. Design, develop, and optimize high-performance inference systems for large-scale LLMs and VLMs, covering inference engines, serving frameworks, and end-to-end deployment pipelines. 2. Build state-of-the-art model inference engines through advanced performance optimization techniques such as compiler-level optimizations, parallel computing, graph fusion, efficient CUDA kernel development, low-precision computation, streaming inference, speculative decoding, and high-concurrency request optimization. 3. Collaborate closely with other research teams to identify performance bottlenecks, conduct in-depth performance analysis, and optimize large models; contribute to the development of model toolchains and the broader technical ecosystem. The base salary range for this position in the selected city is $232560 - $427500 annually.