- Location
- CHN - Minhang, China
- Type
- Full-time
- Department
- Engineering
- Experience
- 2+ years
- Education
- Master
- Source
- Workday
Description
Job Details:
Job Description:
The Role and Impact: As an AI Frameworks Engineer, you will play an integral role in designing and optimizing cutting-edge artificial intelligence software and frameworks. You will contribute to developing distributed algorithms, enhancing deep learning models, and improving AI software performance across diverse hardware backends. By collaborating with researchers and engineers, your work will directly influence Intel's ability to deliver advanced AI solutions, enabling transformative capabilities in the field of artificial intelligence. Business group: This role is part of Intel's Artificial Intelligence engineering group, a team dedicated to developing innovative AI software solutions and frameworks that enhance performance and efficiency across Intel products. The group focuses on advancing AI technologies and creating impactful solutions that empower customers and drive Intel's leadership in the AI industry. Key Responsibilities: - Design, develop, and optimize AI frameworks such as vLLM and PyTorch for high-performance applications. - Implement distributed algorithms, including model/data parallel frameworks and asynchronous data communication in machine learning and deep learning systems. - Transform computational graph representations of neural network models and develop machine learning primitives in mathematical libraries. - Profile distributed deep learning models to identify bottlenecks and propose performance-enhancing solutions. - Collaborate with deep learning researchers to integrate advancements into AI frameworks. - Contribute to open-source projects, ensuring compliance with industry standards and delivering impactful innovations.Qualifications:
Minimum Qualifications: - Master degree of Computer Science or bachelor degree with at least 2 years of domain working experience. - Proficiency in Python and C++ programming languages. - Foundational knowledge of AI frameworks such as vLLM, PyTorch and deep learning concepts. - Demonstrated ability to debug and optimize software for performance. Preferred Qualifications: - Familiarity with performance optimization technologies and techniques. - Experience working with large language models and related inference stacks. - Background in contributing to open-source projects or collaborating within open-source ecosystems. - Strong problem-solving skills and ability to analyze complex technical challenges. - Effective communication skills, including proficiency in written and spoken English. - Familiarity with the GPU architecture and concepts is a big plus Join Intel to explore the forefront of AI innovation, make meaningful contributions to the field, and collaborate with industry leaders to shape the future of artificial intelligence.
Job Type:
College GradShift:
Shift 1 (China)Primary Location:
PRC, ShanghaiAdditional Locations:
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/AWork 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.*
ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.Skills
PythonMachine LearningDeep LearningPyTorchCompliance