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# Research Engineer - AI Systems

- **Company:** Yotta Labs Inc
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, C++ (Programming Language), Profiling, Nvidia CUDA, Distributed Computing Environment, High-Level Architecture, Python (Programming Language), Linux Kernel, Open Source Technology, Performance Tuning, AI Infrastructure, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Information Technology, TensorRT
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/research-engineer-ai-systems-yotta-labs-8610906

## About the Role

* Proficiency in AI programming languages such as Python and C++.
 * Deep understanding of GPU architecture and performance optimization.
 * Experience with CUDA, Triton, ROCm/HIP, or AWS Neuron.
 * 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 in a collaborative, remote environment.
 * A background in computer science, engineering, or a related field is preferred.

Preferred Experience

 * Contributions to open-source AI infra projects like vLLM, SGLang, PyTorch, or Triton.
 * Experience with with FlashAttention, PagedAttention, MoE, RLHF, or distributed AI systems.
 * Publications in top-tier conferences like MLSys, OSDI, SOSP, NSDI, SC, HPCA, or ISCA

## Description

We are seeking a highly motivated AI Systems Research Engineer specializing in Trainium, GPU kernels, and LLM systems optimization. You will work at the intersection of AI Systems, Compiler and Runtime Optimization, Distributed Training & Inference, GPU/Accelerator Kernel Development, and Large Language Model Infrastructure. Your work will directly impact the scalability and performance of AI applications deployed on our platform., * Design and implement high-performance kernels for Attention, MoE, GEMM, collective communication, and quantization.

 * Optimize kernels for NVIDIA, AMD, and AWS Trainium.
 * Develop custom operators and graph optimizations using Neuron SDK, PyTorch/XLA, Torch Dynamo, and Neuron Compiler.
 * Improve performance of vLLM, SGLang, TensorRT-LLM, and custom inference runtimes.
 * Design scalable distributed training and inference solutions across thousands of accelerators.
 * Contribute to open-source projects, publish technical findings and engage with the developer community.


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