AI Frameworks Engineer

Intel•Folsom, CA

19h•Hybrid

About The Position

As an AI Frameworks Engineer at Intel, you will design, develop, and optimize AI software frameworks. You will contribute to advanced AI frameworks, implement distributed algorithms, and work with researchers to deliver high-performance solutions that enhance Intel's AI capabilities and advance machine learning and deep learning technologies. The position is part of a group focused on advancing AI solutions through optimized frameworks and libraries that accelerate artificial intelligence applications. The role is based in Folsom, California, with an additional location in Hillsboro, Oregon, and is eligible for Intel's hybrid work model, which splits time between the assigned Intel site and off-site work.

Requirements

Bachelor's degree in Computer Science, Electrical Engineering, or a related field and at least 1 year of experience in AI frameworks development and optimization, or a master's degree in a relevant field.
At least 6 months of experience with programming languages such as Python or C++.
At least 6 months of experience with AI frameworks such as TensorFlow, PyTorch, or OpenVINO.
At least 6 months of experience applying algorithmic and mathematical skills to machine learning and deep learning.
Strong communication and presentation skills.
Strong organizational and planning skills.
Ability to work independently in a highly matrixed structure.

Nice To Haves

Master's degree in a relevant field with at least 2 years of related experience, or a Ph.D.
Experience optimizing code for hardware backends such as CPUs, GPUs, or accelerators.
Familiarity with distributed computing techniques and tools for AI applications.
Experience with graph-based computation models and performance profiling tools.
Strong communication skills and experience collaborating in cross-functional teams.
Passion for driving innovation and solving complex technical challenges in AI.

Responsibilities

Design, develop, and optimize AI frameworks such as OpenVINO, TensorFlow, and PyTorch.
Implement and enhance distributed algorithms, including model- and data-parallel frameworks, parameter servers, and asynchronous data communication.
Transform computational graph representations of neural network models to improve performance.
Develop high-performance machine learning and deep learning primitives within mathematical libraries.
Profile distributed deep learning models to identify bottlenecks and propose solutions.
Optimize code across different computing hardware backends to maximize performance.
Collaborate with researchers to integrate advances in machine learning and deep learning into frameworks.