AI Frameworks Engineer

Job Description

Intel NPU organization is dedicated to research and development for the future of AI - unprecedented scale for enabling machine intelligence on Edge, desktop, and mobile computers. While achieving a minimal power consumption and tremendous computing power, Intel AI accelerators are targeting daily use for millions of devices. Join the adventure of harnessing the complexity of state-of-art Deep Neural Networks and most advanced AI hardware accelerators in the word.

The Neural Network Compiler Optimization team is looking for a highly motivated Software Engineer with a graph theory background and problem-solving skills. Our team is working to utilize the OpenVINO toolkit and underlying MLIR infrastructure in order to redefine the limits of Neural Networks performance for the Neural Processing Units generations to come.

The successful candidate will be part of our efforts to research and develop graph-based compilation algorithms for broad horizontal scaling for the new Neural Network back-end compiler project and will be cooperating on a daily basis with runtime software, research, infrastructure and front-end teams.

Qualifications

Minimal Qualifications:

• BS/MS in Computer Science or a similar field.

• At least 3-4 years of experience in programming.

• Excellent C++ programming skills.

• Strong production software engineering background, experience with CI, code reviews, paired programming, unit and integration testing.

• Proven track of experience in contributing in large-scale, multi-component software systems.

• Experience in LLVM/MLIR.

Preferred Qualifications:

Experience in one the following:

• compiler technologies, computer vision, numerical modelling, high-performance computing, deep-learning frameworks or algorithms.

• Experience in development of graph-based algorithms, e.g. production solutions based on graph coloring, maximum cut, shortest path, etc.

• Experience in AI hardware accelerators, GPU, heterogeneous architectures software development.

• Experience in mapping between Neural Networks architectures and hardware accelerated inference.

• Python programming skills.

Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.

Inside this Business Group

The Client Computing Group (CCG) is responsible for driving business strategy and product development for Intel's PC products and platforms, spanning form factors such as notebooks, desktops, 2 in 1s, all in ones. Working with our partners across the industry, we intend to deliver purposeful computing experiences that unlock people's potential - allowing each person use our products to focus, create and connect in ways that matter most to them. As the largest business unit at Intel, CCG is investing more heavily in the PC, ramping its capabilities even more aggressively, and designing the PC experience even more deliberately, including delivering a predictable cadence of leadership products. As a result, we are able to fuel innovation across Intel, providing an important source of IP and scale, as well as help the company deliver on its purpose of enriching the lives of every person on earth.

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.

Benefits

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock, bonuses, as well as, benefit programs which include health, retirement, and vacation. Find more information about all of our Amazing Benefits here.

Working Model

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. * Job posting details (such as work model, location or time type) are subject to change.