Research Engineer - LLM Training Infrastructure - Seed Infra

Team Information: The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.

Responsibilities - Conduct research and development on large-scale LLM training infrastructure and efficiency - Design and optimize distributed training strategies for LLMs, including parallelism schemes, computation and communication optimization, and throughput scaling on large GPU clusters - Investigate system reliability and resilience techniques, such as fast checkpointing, fault tolerance, and failure diagnosis for long-running training workloads - Research and optimize network, scheduling, and GPU memory management across the training stack, driving cross-layer performance improvements - Analyze performance bottlenecks in exascale training systems and propose principled, data-driven optimization methods - Bridge cutting-edge research and large-scale production deployment by translating research ideas into scalable, real-world AI infrastructure solutions

Minimum Qualifications - Experience with large-scale distributed training for LLMs - Strong programming skills in Python and/or C++ - Strong background in ML systems / training infrastructure development - Proficiency in parallelism strategies (DDP, FSDP, model/pipeline/expert parallelism) - Solid understanding of training stack internals (PyTorch, CUDA, NCCL) - Experience in performance optimization (memory, communication, throughput)

Preferred Qualifications - Hands-on experience with distributed training frameworks and large-scale LLM infrastructure - Experience leading or mentoring engineering teams or cross-functional projects - Publications in top-tier AI, systems, or HPC conferences (ICML, OSDI, SOSP, NSDI, SIGCOMM, MLSys) or strong open-source contributions - Familiarity with benchmarking AI accelerators or large-scale LLM evaluation (e.g., ByteMLPerf)