Job Details:
Job Description:
We are looking for a dynamic compiler engineer to design, develop and optimize for performance, of compilers for Intel’s deep learning accelerators. In this role, you will work on cutting edge technologies, with a cross-geo team to design, develop, optimize and benchmark to meet performance goals of the Deep Learning SW stack with a specific focus on LLVM and MLIR-based compilers. Design and develop code-generation and optimization techniques for compilers – both machine-independent and machine-specific. Profile various ML workloads and identify optimization opportunities in the compilers. Innovate and develop tools to aid performance analysis and tuning, with special focus on compilers.
Qualifications:
Job Type:
Experienced Hire
Shift:
Shift 1 (India)
Primary Location:
India, Bangalore
Additional Locations:
Business group:
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.
Posting Statement:
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.
Position of Trust
N/A
Work Model for this Role
This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. In certain circumstances the work model may change to accommodate business needs.
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