Join our compiler team and contribute to the development of an MLIR-based compiler that drives performance improvements on Intel deep learning accelerators.
This compiler delivers significant performance gains across Intel products and directly impacts cutting-edge deep learning workloads.
In this role, you will:
Required:
5+ years of experience in C++ and familiarity with modern software development practices.
Background in high-performance computing or parallel programming.
Strong analytical and problem-solving skills.
Experience with development on Linux.
Advantage:
Strong experience with compiler design and middle-end optimizations, preferably using MLIR and LLVM.
Experience with code generation, performance analysis, and tuning for hardware accelerators.
Knowledge of dataflow graphs, IR transformation, and optimization techniques.
Experienced Hire
Shift 1 (Israel)
Israel, Petah-Tikva
Israel, Haifa
The Data Center & Artificial Intelligence Group (DCAI) is at the heart of Intel’s transformation from a PC company to a company that runs the cloud and billions of smart, connected computing devices. The data center is the underpinning for every data-driven service, from artificial intelligence to 5G to high-performance computing, and DCG delivers the products and technologies—spanning software, processors, storage, I/O, and networking solutions—that fuel cloud, communications, enterprise, and government data centers around the world.
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
N/A
Work Model for this Role
This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.
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