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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…
FuriosaAI is seeking a Senior Solution Architect to bridge the gap between technical teams and customers by designing AI inference architectures and optimizing pipelines on their proprietary NPU hardware. The role involves conducting PoCs, performing performance benchmarks, and providing technical support to ensure successful deployment of AI applications.
A senior technical expert bridging business teams, customers, partners, and the developer community: analyzes customer pain points, designs optimal architectures, runs PoCs and benchmarks, and builds AI inference programs optimized on FuriosaAI NPU hardware and SDK. Requires Python and 3+ years of engineering experience.
AI Research Engineer developing proprietary LLMs for agentic tasks, RL post-training pipelines, and frontier AI research at a Korean AI chip company, primarily using PyTorch and deep learning frameworks.
As an AI Research Engineer Intern at FuriosaAI, you will conduct research on advanced AI models, focusing on areas like LLMs and RL, and contribute to the development of a proprietary AI compute platform. You will work with PyTorch and other deep learning tools to build high-performance AI solutions.
Build and optimize a high-performance inference engine for LLMs and multimodal models on FuriosaAI’s NPUs, collaborating with compiler and hardware teams to maximize throughput and efficiency.
Builds and optimizes an agent system framework for AI-driven automation, integrating LLM-based reasoning, tool execution, and orchestration in a production environment.
Build and refine AI-assisted engineering workflows for FuriosaAI’s compiler team, optimizing development, debugging, and CI using LLM tools and automation to boost team productivity.
FuriosaAI is seeking a Software Engineer to develop the front-end of their AI compiler, focusing on model ingestion, graph-level optimizations, and the design of a tensor-level kernel language. The role involves translating deep learning models into internal representations and collaborating on compiler infrastructure to maximize performance on AI hardware.
Develops autonomous agent systems that solve complex engineering problems via AI-driven exploration, optimization, and self-improvement, integrating LLM post-training and multi-agent workflows for production-grade solutions.
Design and deploy AI/LLM models on FuriosaAI’s RNGD NPU using the Furiosa SDK, running POCs, benchmarks, and debugging for US customers while translating technical capabilities into business value.
Solution Architect for APAC at FuriosaAI, an AI chip maker: architects end-to-end enterprise/cloud AI solutions around the company's AI accelerators and server LLM deployments, leads pre-sales engagements and PoCs, and mentors a team of solution architects. Key tech: GPGPUs, AI accelerators, LLM serving, MLPerf/vLLM benchmarking, data center and distributed systems.
Software engineer on FuriosaAI's MLSys team enabling frontier LLMs on the company's NPU chips: analyzing model architectures, writing and optimizing Tensor Contraction Language (TCL) kernels (attention, MoE), integrating models into the Furiosa-LLM inference stack, and validating correctness and performance. Core stack: Python, PyTorch, TCL DSL, with Rust/C++ code reading and vLLM/SGLang technique
Software engineer on FuriosaAI's compiler team in Seoul who owns the performance of critical AI kernels in Furiosa-LLM: analyzing LLM serving workloads, finding bottlenecks, and writing highly optimized kernels in TCL (Tensor Contraction Language), plus developing scheduling and specialization techniques for dynamic serving workloads.
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