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

Requirements:

• 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 (Py Torch, 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)