Kempner Fellowship Program in Research Engineering

Our fellowship program offers a comprehensive, hands-on learning experience for recent graduates interested in preparing for successful careers in the field of AI/ML engineering.

Photo credit: Anthony Tulliani

Program summary

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

Engineering Fellows will be mentored by a Kempner Institute Investigator and interact directly with a member of the Kempner Institute Research and Engineering (R&E) 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.

Each Engineering Fellow will also be assigned a mentor from the R&E team and will be incorporated in R&E team activities, overseen by our Lead Technical Project Manager. 

Products resulting from the fellow’s activities such as code, models, or datasets, may be published on Kempner Institute public channels, including GitHub, Hugging Face, or our research blog as determined by the faculty mentor and institute leadership. 

Learning opportunities

Participants will have the opportunity to engage in research projects that will enhance their skills and understanding of advanced technologies. 

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 multi-node distributed GPU training to enhance the performance of distributed AI/ML workflows. 
  • 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.

Details

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

Harvard is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.

  • 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.

  • Applicants should be within three years of graduation from a bachelor’s or master’s degree at the time of application. 
  • The fellowship requires a minimum 6-month commitment, which is typically renewed for an additional 6-month term based on satisfactory performance and mutual interest. 
  • The fellowship program is typically a full-time (35-hour per week) position. 

Candidate applications are reviewed on a rolling basis throughout the year. 

The hiring committee will select applicants based on the nature of the projects and their qualifications as described in their application.   

Applicants should submit their application, cover letter and curriculum vitae using the application link below.

Apply now