Spring/Summer 2027 PhD Large Language Model Engineer Co-op
Core
Conduct research on scalable training and inference of large language models, focusing on ML systems and HPC techniques.
Role type
PhD Research Intern (LLM Systems & HPC)
Builds
Distributed training frameworks, model parallelism strategies, and efficient resource management for large-scale AI workloads.
Domain
AI Infrastructure / High-Performance Computing
Deliverable
research
Required skills
Machine learning, distributed systems, parallel computing, deep learning frameworks, Python, C++, performance profiling, GPU/ASIC knowledge, HPC techniques, AI compiler optimizations
Preferred skills
Experience with large-scale LLM training, AI acceleration, top-tier conference publications
Technologies
PyTorch, TensorFlow, JAX, Triton, XLA, MLIR, MPI, CUDA, NCCL, FSDP, ZeRO, DeepSpeed, Megatron-LM
Responsibilities
Develop and optimize distributed training frameworks; Explore hardware-aware optimizations (algorithm-hardware co-optimization, quantization); Implement and benchmark ML system optimizations; Collaborate to publish findings in top-tier conferences.
Seniority
PhD Student, Research Intern
