Job ID: JR0231291

Job Category: Software Engineering

Primary Location: Santa Clara, CA US

Other Locations:

Job Type: College Grad

AI Frameworks Engineer

Job Description
Conduct design and development to build and optimize deep learning software. Design, develop and optimize for deep learning training and inference frameworks. Implement various distributed algorithms such as model/data parallel frameworks, parameter servers, dataflow based asynchronous data communication in deep learning frameworks. Transform computational graph representation of neural network model. Develop deep learning primitives in math libraries. Profile distributed DL models to identify performance bottlenecks and propose solutions across individual component teams. Optimizing code for various computing hardware backends. Interacting with deep learning researchers and experience with deep learning frameworks. The ideal candidate should exhibit the following behavior skills: Strong communication skills Work well in a team environment

Qualifications

You must possess the below minimum qualifications to be initially considered for this position. Experience listed below would be obtained through a combination of your school-work/ classes/ research and/or relevant previous job and/or internship experiences. Minimum Qualifications: Masters or PhD in Computer Science or Computer Engineering or Electrical Engineering or AI or computer vision or SW Engineering or Physics or Chemistry or Mechanical Engineering or related technical discipline . 2+ years of experience with the following skills: Excellent Programming skills in languages like Python, C/C++ and CUDA Low level programming and performance optimization skills for CPU and GPU including code generation, performance optimization, distributed compute and resource management. Understanding of Deep Learning algorithms Familiarity with DL frameworks (e.g. TensorFlow, PyTorch, Mxnet, etc.) Preferred Qualifications: Experience in Machine Learning infrastructure development and optimization (framework, ML pipeline, deployment) Experience in CUDA, OpenCL and GPU programming including compute kernel development and optimizations Experience in Machine Learning acceleration through model compression, quantization and distillation. Experience in high performance computing, high performance networking, distributed computing algorithms and systems Experience in cloud computing and system integration

Inside this Business Group

The Machine Learning Performance (MLP) division is at the leading edge of the AI revolution at Intel, covering the full stack from applied ML to ML / DL and data analytics frameworks, to Intel oneAPI AI libraries, and CPU/GPU HW/SW co-design for AI acceleration. It is an organization with a strong technical atmosphere, innovation, friendly team-work spirit, and engineers with diverse backgrounds. The Deep Learning Frameworks and Libraries (DLFL) department is responsible for optimizing leading DL frameworks on Intel platforms. We also develop the popular oneAPI Deep Neural Network Library (oneDNN), and new oneDNN Graph library. Our goal is to lead in Deep Learning performance for both the CPU and GPU. We work closely with other Intel business units and industrial partners. 

Intel strongly encourages employees to be vaccinated against COVID-19. Intel aligns to federal, state, and local laws and as a contractor to the U.S. Government is subject to government mandates that may be issued. Intel policies for COVID-19 including guidance about testing and vaccination are subject to change over time.

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.

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.

USCollege GradJR0231291Santa ClaraMachine Learning Performance (MLP)