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Harvard University Fellow in Research Engineering, Kempner Institute in Cambridge, Massachusetts


Title Fellow in Research Engineering, Kempner Institute

School Faculty of Arts and Sciences

Department/Area Kempner Institute for the Study of Natural and Artificial Intelligence

Position Description

The Engineering Fellowship Program at Kempner Institute at Harvard University ( offers a structured opportunity for recent graduates (fellows) to further their experience in AI/ML engineering. The program offers fellows a comprehensive, hands-on learning experience that prepares them for a successful career in the AI/ML field.

Engineering Fellows will interact directly with a member of the Kempner Institute Research Engineering team to advance their skills and understanding of advanced technologies. This includes developing cutting-edge AI/ML models and datasets; learning how to take advantage of unparalleled computing resources in the academic environment by optimizing AI/ML models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing new models of brain circuits and function; and learning software engineering best practices including how to develop and disseminate reliable, reproducible open-source AI/ML scientific software packages.

Products resulting from the fellows activities such as code, models, or datasets, may be published on Kempner Institute public channels, including GitHub ( , Hugging Face ( , or our Research Blog ( .

The fellowship program is a full-time (35-hour per week) position. Fellows are appointed for a minimum 6 month commitment, which is typically renewed for an additional 6 month term based on satisfactory performance and mutual interest.

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.

Basic Qualifications

  • 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, generative models, 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.

    Additional Qualifications

Applicants should be within three years of graduation from a bachelor’s or master’s degree at the time of application.

Special Instructions

Applicants should submit a resume and a cover letter which:

  • Briefly describes your educational background (50 words).

  • Describes a project or experience where you used Python for coding or developing AI/ML models (100 words max).

  • Describes any hands-on experience you have in data preprocessing, feature engineering, and model evaluation (100 words max).

  • Lists any additional skills or technologies you are proficient in (e.g., C++, Julia, AWS , TensorFlow, etc.) (50 words or less).

Cover letters should also include a rating for your:

  • A. Proficiency in Python

  • B. Experience with Deep Learning Frameworks (e.g., PyTorch)

  • C. Familiarity with HPC including running serial or distributed jobs

  • D. Familiarity with AI/ML fields

Using the following ratings:

  • (1) Beginner – little to no experience

  • (2) Intermediate – have used it in projects

  • (3) Advanced – extensive experience and deep understanding in multiple successful projects

    Contact Information

Sarah Leinicke

Contact Email

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required

Maximum Number of References Allowed


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