Intel Foundational AI Research Scientist Intel contract employee 

United States, California, San Jose 

974989176

As a Research Scientist in Intel AI Lab, you will develop machine learning and optimization techniques to solve problemssemiconductor design and manufacturing. You will publish your findings,benchmarks, build open-source tools, andtechnology transfers.

with shands-on implementation skills. You will be working on complex, industrial scale problems that involve multi-constrained optimization.The solutions you will explore will require core machine learning techniques as well as classical combinatorial optimization and search techniques.

In this role, examples of your responsibilities are:

• Develop foundational machine learning capabilities for diverse applications.

• Develop machine learning techniques to accelerate optimization problems.

• Impact semiconductor design and manufacturing through research.

• Publish results in top-tier conferences and journals.

• Establish new industrial and academic benchmarks.

• Build open-source tools andfacilitatetechnology transfers.

• Collaborate with teams to integrate research into commercial design flows.

You mustthe below minimum education requirements and minimum required qualifications to be initially considered for this position. Relevant experience can be obtained through schoolwork, classes, projectwork, internships, and/or military experience.preferred qualifications are in addition to therequirements and are considered a plus factor in

Minimum Qualifications:

• PHD with 1+ years of research experience in Electrical Engineering, Computer Science, Information Systems, or STEM-related field.

• + years of experience and solid background in modern machine learning architectures: Transformers, Graph Neural Networks, Diffusion models, etc.

• + years of experience and solid coding skills in Python and C++.

• + years of experience and solid experience with machine learning frameworks like PyTorch.

Preferred Qualifications:

• eep learning on graphs (e.g., general graphs, circuit graphs, molecular graphs, or trees).

• lassical search and optimization techniques like Genetic Algorithms, Monte-Carlo Tree Search,Djikstra’sAlgorithm, etc.

• EDA tools, either commercial oropen-source

• Publications in top-tier venues in AI for chip design