Research Engineer Intern focused on AI Systems at Yotta Labs. Work on optimizing GPU kernels and LLM infrastructure in a remote setting.
Responsibilities
Implement and optimize compute kernels for Attention, GEMM, MoE, and quantization on NVIDIA, AMD, or AWS Trainium.
Build custom operators using CUDA, Triton, ROCm/HIP, or the Neuron SDK with PyTorch/XLA.
Profile and improve inference performance in vLLM, SGLang, and our custom runtimes — kernel fusion, scheduling, KV-cache and memory optimizations.
Build benchmarks, chase down performance regressions, and turn profiler traces into concrete speedups.
Ship code upstream to open-source AI infrastructure projects, with tests and documentation.
Requirements
Currently pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, or a related field
Solid programming skills in Python and familiarity with C++
Understanding of GPU/accelerator architecture fundamentals (memory hierarchy, parallelism, occupancy) from coursework, research, or projects
Experience writing CUDA, Triton, ROCm/HIP, or Neuron kernels — class projects and personal projects count
Strong understanding of AI frameworks (e.g., PyTorch, Dynamo, LMCache), model architectures and profiling tools (e.g. Nsight, ROCm Profiler, or Neuron Profiler)
Strong problem-solving skills and the ability to work independently in a collaborative, remote environment.
Benefits
Competitive internship compensation
Flexible remote work environment
Direct mentorship from engineers from leading institutions and tech companies
Fast path to a full-time return offer for top performers
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