Software Engineer, AI Application
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
Develop a comprehensive AI reference application and maintain internal AI services to accelerate real-world deployment of RNGDs.
Responsibilities
Develop high-impact AI applications and technical resources to enable the success of the global developer community.
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Develop enterprise-focused, high-quality reference applications and sample code that enable developers to effectively understand and utilize FuriosaAI’s SW stack and integrations.
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Drive end-to-end application enablement such as RAG systems, agentic systems, and RL inference engines on FuriosaAI accelerators, creating reproducible guides and reference implementations.
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Evaluate applications against real-world scenarios to validate performance, reliability, and usability of RNGD deployments.
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Support live application demos and collaborate with the global developer community through GitHub and open-source channels to improve real-world usability.
Minimum Qualifications
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BS in Artificial Intelligence, Computer Science, Electrical Engineering, or a related field.
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Experience in full-stack (frontend and backend) development of AI services.
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Experience collaborating across engineering, product, and design teams to align software development with product requirements.
Preferred Qualifications
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MS or PhD in Artificial Intelligence, Computer Science, Electrical Engineering, or a related field.
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Experience in developing and maintaining AI products, including running A/B tests and operating services under high traffic.
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Experience in open-source projects on AI applications such as agentic systems, RAG, or multimodal pipelines.