Research Engineer – AI Systems

Yotta Labs

AI Systems Research Engineer at Yotta Labs focusing on optimizing AI systems and GPU architecture for multi-silicon computing. Join a visionary team rethinking AI infrastructure through innovation and collaboration.

Posted 6/29/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and optimizing high-performance kernels for AI applications, with a strong focus on GPU architecture and distributed training solutions. Proficient in AI programming languages and frameworks, contributing to open-source projects and engaging with the developer community.

Highest-signal resume keywords

Python ProgrammingC++ ProgrammingGPU Architecture OptimizationCUDA ExperienceAI Frameworks Proficiency

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

High-Performance Kernel DesignAttention MechanismsMoE ImplementationGEMM OptimizationQuantization TechniquesCustom Operator DevelopmentGraph OptimizationDistributed Training SolutionsPerformance Profiling ToolsModel Architecture Understanding

Soft Skills

Problem-Solving SkillsCollaborative Work

Tools & Technologies

Neuron SDKPyTorchTorch DynamoNeuron CompilerTensorRT-LLMSGLangNsightROCm ProfilerNeuron ProfilerAWS Neuron

Industry Keywords

Open-Source ContributionTechnical PublishingDeveloper Community EngagementRemote Work EnvironmentComputer Science Background

Tech Stack

Tools & technologies

AWSPythonPyTorch

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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

Benefits

Comp & perks

Competitive compensation with equity
Flexible, remote work environment that values innovation and autonomy