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.
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.
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
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
