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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 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 Role 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. Key Responsibilities Design and implement efficient kernels on FuriosaAI’s kernel programming stack (including vISA, TCL), targeting Tensor Contract Processor (TCP) architectures. Diagnose and optimize kernel performance with profiling tools and roofline analysis for each RNGD-accelerated AI model. Develop and apply automated kernel generation and optimization for AI workloads. Build diagnostic tools or testbeds for robust and reliable kernel validation. Drive end-to-end programming enablement on RNGDs, creating reproducible guides and reference implementations. Minimum Qualifications BS in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field. Experience in low-level systems programming targeting XPU (e.g., NPU, GPU) architectures. Experience collaborating across engineering, research, and product teams to align software development with product requirements. Preferred Qualifications MS or PhD in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field. Experience in optimizing high-performance kernels on AI accelerators (e.g., GPU, TPU) for AI products. Understanding of XPU architecture (computation patterns, data movement) and software-hardware co-optimization strategies. Experience in open-source or research projects on AI model architectures such as Diffusion, Mamba, and VLA. Experience in designing efficient deep learning architectures and developing algorithms for AI applications. Contact recruit@furiosa.ai
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.