Research Engineer Intern – AI Systems

Yotta Labs

Research Engineer Intern focused on AI Systems at Yotta Labs. Work on optimizing GPU kernels and LLM infrastructure in a remote setting.

Posted 8/2/2026internshipRemote • 🇺🇸 United StatesEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in implementing and optimizing compute kernels for AI frameworks, with strong programming skills in Python and experience in GPU architecture. Proficient in building custom operators and profiling performance in collaborative environments.

Highest-signal resume keywords

CUDA ProgrammingPyTorch FrameworkGPU Architecture FundamentalsPerformance Profiling ToolsCustom Operator Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingC++ ProgrammingCUDATritonROCm/HIPNeuron SDKAI FrameworksModel ArchitecturesKernel OptimizationMemory Optimization

Soft Skills

Problem-SolvingCollaborationIndependence

Tools & Technologies

NsightROCm ProfilerNeuron Profiler

Industry Keywords

Compute KernelsAttentionGEMMMoEQuantizationInference PerformanceBenchmarkingOpen-Source AI Infrastructure

Tech Stack

Tools & technologies

AWSPythonPyTorch

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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