Job Details:

Job Description: 

We are seeking a machine learning researcher with strong hands-on implementation skills to join our team as a Foundational AI Research Intern. You will work on complex, industrial-scale problems involving multi-objective constrained optimization. The solutions you explore will require core machine learning techniques as well as classical combinatorial optimization and search techniques.

  

In this role, examples of your responsibilities are:  

• Conduct research on complex, industrial-scale problems involving multi-objective constrained optimization.
• Develop and implement solutions using core machine learning techniques and classical combinatorial optimization and search techniques.
• Collaborate with team members to explore innovative approaches to solve challenging problems.
• Contribute to the development of research papers and publications in top machine learning conferences

Qualifications:

You must possess the below minimum education requirements and minimum required qualifications to be initially considered for this position. Relevant experience can be obtained through schoolwork, classes, project work, internships, and/or military experience. Additional preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.

Minimum Qualifications:  

• Must be pursuing a PhD in Electrical Engineering with 1+ years of research experience in Electrical Engineering, Computer Science, Information Systems, or STEM-related field.
• 1+ years of experience and solid background in modern machine learning architectures such as Transformers, Graph Neural Networks, and Diffusion models.
• 1+ years of experience and strong coding skills in Python and C++.
• 1+ years of experience with machine learning frameworks like PyTorch.
• At least 1 publication in top machine learning conferences such as NeurIPS, ICML, or ICLR.

Preferred Qualifications:

• Prior work on deep learning on graphs (e.g., general graphs, circuit graphs, molecular graphs, or trees).
• Familiarity with classical search and optimization techniques like Genetic Algorithms, Monte-Carlo Tree Search, and Dijkstra's Algorithm.
• Experience with EDA tools, either commercial or open-source.
• Publications in top-tier venues in AI for chip design or combinatorial optimization

          

Job Type:

Student / Intern

Shift:

Shift 1 (United States of America)

Primary Location: 

Virtual US

Additional Locations:

Business group:

Enable amazing computing experiences with Intel Software continues to shape the way people think about computing – across CPU, GPU, and FPGA architectures. Get your hands on new technology and collaborate with some of the smartest people in the business. Our developers and software engineers work in all software layers, across multiple operating systems and platforms to enable cutting-edge solutions. Ready to solve some of the most complex software challenges? Explore an impactful and innovative career in Software.

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/A

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:

https://jobs.intel.com/en/benefits

Annual Salary Range for jobs which could be performed in the US:

$63,000.00-$166,000.00

Salary range dependent on a number of factors including location and experience.

Work Model for this Role

This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on business need. This role may also be available as 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.

The application window for this job posting is expected to end by 01/28/2025