Software Engineer, Kernel Programming
About FuriosaAI
FuriosaAI builds high-performance, high-efficiency AI compute for the Inference Era. Founded in 2017 by veteran semiconductor and AI algorithm engineers, Furiosa operates globally with offices in Korea and Silicon Valley, along with a compiler-focused R&D lab in Lisbon.
Our vision is to make AI computing sustainable, enabling access to powerful AI for everyone on Earth. We solve the AI hardware energy and operational cost crisis at the architectural level, rather than through brute force, building the world's first truly AI-native compute platform to unlock the full potential of artificial intelligence for every enterprise.
About the job
Lead the integration of diverse AI models including VLA, Vision, and Multimodal architectures by utilizing our kernel programming language to ensure both accuracy and performance while keeping the stack ready for developers to use.
Responsibilities
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Design and implement efficient kernels on FuriosaAI’s kernel programming stack (including vISA, TCL), targeting Tensor Contract Processor (TCP) architectures.
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Diagnose and optimize kernel performance with profiling tools and roofline analysis for each RNGD-accelerated AI model.
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Develop and apply automated kernel generation and optimization for AI workloads.
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Build diagnostic tools or testbeds for robust and reliable kernel validation.
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Drive end-to-end programming enablement on RNGDs, creating reproducible guides and reference implementations.
Minimum Qualifications
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BS in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field.
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Experience in low-level systems programming targeting XPU (e.g., NPU, GPU) architectures.
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Experience collaborating across engineering, research, and product teams to align software development with product requirements.
Preferred Qualifications
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MS or PhD in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field.
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Experience in optimizing high-performance kernels on AI accelerators (e.g., GPU, TPU) for AI products.
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Understanding of XPU architecture (computation patterns, data movement) and software-hardware co-optimization strategies.
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Experience in open-source or research projects on AI model architectures such as Diffusion, Mamba, and VLA.
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Experience in designing efficient deep learning architectures and developing algorithms for AI applications.