Kempner Institute Engineering Internship Program

Each intern will be assigned a mentor from the Research Engineering team, and their efforts will be overseen by our Lead Technical Project Manager. 

As an intern, participants will have the opportunity to engage in projects that will enhance their skills and understanding of advanced technologies. Products resulting from the interns’ work, such as code, models, or datasets, may be published on Kempner Institute public channels, including GitHub, Hugging Face, or our Research Blog. 

Training opportunities may include:

• Contribute to developing and improving innovative AI/ML models and datasets. 
• Help to optimize AI/ML models to improve accuracy and computational efficiency, ensuring they are scalable and robust.
• Participate in the creation or optimization of LLMs, including developing evaluation benchmarks tailored to various tasks.
• Learn and apply best software engineering practices suited for AI and ML development.
• Design and implement AI workflows that run efficiently on AI HPC clusters.
• Enhance the reproducibility of AI/ML workflows, ensuring they are ready for both on-premises and cloud environments (cloud readiness).
• Engage with the open science community by helping to develop and disseminate open-source AI/ML scientific software packages.
• Gain experience in distributed GPU training to enhance the performance of distributed AI/ML workflows. Learn about the latest in high-performance computing infrastructure, including networking, storage solutions, and GPU technologies.
• Work on the numerical aspects of AI/ML algorithms to maximize efficiency and performance.
• Get hands-on experience with data engineering tasks such as managing data warehouses, data lakes, and data processing pipelines.
• Develop skills in technical project management.

To be eligible for the engineering internship program, you must be either: a Harvard undergraduate, an undergraduate or graduate student enrolled in a non-Harvard degree-granting program, or a recent college graduate (within three years of graduation).

Basic Qualifications:

• Basic proficiency in coding (Python) and deep learning frameworks (PyTorch) with a drive to enhance these skills.
• Familiarity with one of the AI/ML fields like Natural Language Processing, Computer Vision, Reinforcement Learning, GANs, or a strong interest in exploring them.
• Basic data preprocessing, feature engineering, and model evaluation, or a strong willingness to gain hands-on experience.
• Eagerness to learn HPC concepts, including parallel computing, distributed systems, and optimization.
• Analytical skills, problem-solving abilities, and a growth mindset.

For enrolled Harvard college students:

• Applicants must be enrolled full-time in a degree granting program at Harvard College. 
• Academic term: the program requires a commitment of 10-15 hours per week for the duration of the academic term. 
• Summer: the program requires a commitment of 35 hours per week for 10 weeks. 
• Students who demonstrate successful performance may be able to extend the internship for additional academic or summer terms. 

For students enrolled in a non-Harvard degree granting program, or recent college graduates:

• Applicants should be within three years of graduation at the time of application. 
• The internship program is a full-time position, requiring 35 hours of work per week. 
• The internship requires a 6 month commitment, renewable for an additional 6 month term based on performance and interest. 

Please note: The program is fully on-site, in person in the Kempner Institute, 6th floor, Science and Engineering Complex in Allston, MA.  Remote work is not possible in this position.  Applicants must be legally eligible to work in the United States. We are not able to provide visa sponsorship for this position. 

Application Process

To apply, please complete this application form. You will be asked to upload your resume at the bottom of the application form. Applications are accepted on a rolling basis.

The hiring committee will select eligible applicants based on the nature of the projects and their qualifications. The selection process includes two interviews:

Light Technical Interview: This interview round assesses the applicant’s BASIC understanding of AI/ML research and engineering, and includes BASIC data structure and algorithm coding questions.

Final Interview: Conducted with a senior engineer or a member of the Kempner Institute Leadership team, focusing on the applicant’s fit within the team and their potential contributions to the Kempner Institute projects.

Compensation

This is a paid internship position.