FuriosaAI

FuriosaAI and OpenAI showcase the future of sustainable enterprise AI. Read the announcement.

Software - AI Software Engineer (Platform Software)

Seoul, South Korea (Global Remote Available) (On-site)

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About the Job

  • FuriosaAI is looking for passionate AI Software Engineers to join our Platform Team. You will participate in the research and development of models optimized for our NPU accelerator.

  • Our team builds the production-grade, streamlined AI software that makes up our SDK. This includes the runtime, LLM serving framework, and PyTorch models/extensions.

  • Your work on these critical parts of the SDK will directly enable AI developers to efficiently deploy optimized AI models on FuriosaAI NPUs.

Responsibilities

  • Develop and optimize DNN model implementations in PyTorch for FuriosaAI's Tensor Contraction Processor (TCP) architecture

  • Analyze the features, implementations, CUDA and Triton kernels of existing AI model inference frameworks such as vLLM, TensorRT-LLM, and DeepSpeed-MII

  • Research and implement generative AI models, parallelism strategies, and inference techniques to improve performance and efficiency

  • Collaborate closely with the compiler team to optimize and enable models.

Minimum Qualifications

  • BS degree in Computer Science, Engineering, or a related field, or equivalent industry experience

  • Proficiency in Python programming skill

  • Experience in developing AI models in DNN frameworks (e.g., PyTorch)

  • Solid understanding of machine learning, deep learning, natural language processing (NLP), and/or generative AI models

  • Strong communication skills with the ability to collaborate effectively across cross-functional teams

Preferred Qualifications

  • Hands-on experience with PyTorch 2.0 technologies (e.g., TorchDynamo) or DNN compiler technologies, such as Triton and MLIR

  • Proficiency in C++/CUDA or Rust programming skills

  • Hands-on experience deploying and optimizing large-scale ML models in production

  • Hands-on experience in model training and fine-turning of pre-trained models

  • Experience in LLM inference frameworks: vLLM, TensorRT-LLM, and DeepSpeed-MII

  • Strong background in model quantizations and model evaluations

  • Strong background in machine learning, generative AI, and model evaluation techniques

  • Proven track record of contributing to open-source projects

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