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Job Summary

The Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University is a new non-profit research institute focused on unraveling the basis of intelligence. We are building one the largest academic computing clusters in the world to support research in machine learning, artificial intelligence, cognition, and computational neuroscience.

We are deeply committed to open-source ML research and are looking for innovative research engineers to join us to build new, open-source models. Ideal candidates will have experience in training large language, generative or protein folding models in an open-source or research setting.

Research engineers will join a vibrant and inclusive community of Harvard students, faculty, researchers and software engineers who are studying intelligence from biological, cognitive, engineering, and computational perspectives.

Summary

With expert level knowledge in the field of machine learning, lead the development of new and innovative machine learning algorithms and models. Provide guidance in the strategic direction of research projects. Drive innovation and leverage opportunities to improve collaboration.

Position Description

Core Duties:

• Provide expert knowledge in the design, planning, and implementation of open-source distribution of research projects in deep learning or other computational models.
• Coordinate complex projects with other researchers and engineers.
• Build deep understanding of research activities; disseminate new resulting knowledge. and anticipate future research needs.
• Build and maintain relationships with faculty, researchers, students and technical staff to ensure a high level of collaboration.
• Set standards for internal code design and development guides.
• Publish or present research in top tier conferences or journals.
• Adapt or develop new machine learning methods to exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).
• Remain current in the innovations in the field and leverage what you can to move the field forward.

Basic Qualifications

• 8 years of post-secondary experience (can be a combination of education and work experience)
• Expert level knowledge of machine learning and distributed computing concepts
• Significant experience training large, multi-node generative models
• Expert software development skills
• Expert prototyping skills to implement preliminary research ideas
• Expert level experience with deep learning frameworks
• Exceptional collaboration skills
• Multiple publications in top-tier AI journals or conferences
• Minimum of 3 years' experience leading research project/s

Additional Qualifications and Skills

• Experience developing open-source projects strongly preferred.

Additional Information

Harvard University supports a hybrid workplace model which will actively support some remote work. Specific days and schedules for on-site work and remote work will be discussed during the interview process. Please note hybrid workers must reside in a state where Harvard is registered to do business (CA, CT, GA, IL, MA, MD, ME, NH, NJ, NY, RI, VA, VT, and WA).

The health of our workforce is a priority for Harvard University. With that in mind, we strongly encourage all employees to be up to date on CDC-recommended vaccines.

Benefits

We invite you to visit Harvard's Total Rewards website to learn more about our outstanding benefits package, which may include:

• Paid Time Off: 3-4 weeks of accrued vacation time per year (3 weeks for support staff and 4 weeks for administrative/professional staff), 12 accrued sick days per year, 12.5 holidays plus a Winter Recess in December/January, 3 personal days per year (prorated based on date of hire), and up to 12 weeks of paid leave for new parents who are primary care givers.
• Health and Welfare: Comprehensive medical, dental, and vision benefits, disability and life insurance programs, along with voluntary benefits. Most coverage begins as of your start date.
• Work/Life and Wellness: Child and elder/adult care resources including on campus childcare centers, Employee Assistance Program, and wellness programs related to stress management, nutrition, meditation, and more.
• Retirement: University-funded retirement plan with contributions from 5% to 15% of eligible compensation, based on age and earnings with full vesting after 3 years of service.
• Tuition Assistance Program: Competitive program including $40 per class at the Harvard Extension School and reduced tuition through other participating Harvard graduate schools.
• Tuition Reimbursement: Program that provides 75% to 90% reimbursement up to $5,250 per calendar year for eligible courses taken at other accredited institutions.
• Professional Development: Programs and classes at little or no cost, including through the Harvard Center for Workplace Development and LinkedIn Learning.
• Commuting and Transportation: Various commuter options handled through the Parking Office, including discounted parking, half-priced public transportation passes and pre-tax transit passes, biking benefits, and more.
• Harvard Facilities Access, Discounts and Perks: Access to Harvard athletic and fitness facilities, libraries, campus events, credit union, and more, as well as discounts to various types of services (legal, financial, etc.) and cultural and leisure activities throughout metro-Boston.

Work Format

Hybrid (partially on-site, partially remote)


Commitment to Equity, Diversity, Inclusion, and Belonging
Harvard University views equity, diversity, inclusion, and belonging as the pathway to achieving inclusive excellence and fostering a campus culture where everyone can thrive. We strive to create a community that draws upon the widest possible pool of talent to unify excellence and diversity while fully embracing individuals from varied backgrounds, cultures, races, identities, life experiences, perspectives, beliefs, and values.

EEO Statement
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.

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