Position Description

About the Position:

The Center for Computational Biomedicine (CCB) at Harvard Medical School (HMS) is looking for a Machine Learning Engineer with expertise in developing large language models (LLMs) to advance CCB?s mission to leverage data and computation to transform research and education, and to improve health outcomes. CCB provides computational and analytic resources to advance scientific discovery within HMS through its multi-disciplinary team of computational and quantitative scientists who work on collaborative projects both within the center and with members of the HMS community. The selected candidate will play a pivotal role in advancing the center's mission to harness the power of computational techniques in the field of medicine. By developing medical LLMs, the engineer will contribute to educating the next generation of medical students and enhancing clinical decision-making processes.

Key Responsibilities:

• Develop, implement, and optimize medical large language models tailored to the needs of medical education and clinical decision support.
• Collaborate with interdisciplinary teams comprising biologists, clinicians, and data scientists to understand domain-specific requirements and translate them into computational solutions.
• Stay updated with the latest advancements in deep learning and machine learning to ensure the models developed are state-of-the-art.
• Develop infrastructures for data transformation and ingestion.
• Build AI models that make predictions based on large quantities of data.
• Explain the usefulness of the AI models created to stakeholders.
• Transform machine learning models into APIs to interact with other applications.

Basic Qualifications

• A Master's or PhD in Computer Science, Computational Biology, or a related field.
• Minimum of seven years? post-secondary education or relevant work experience, education will count towards experience.
• Minimum of 3 years of hands-on experience in developing complex deep learning solutions to tackle scientific challenges.

Additional Qualifications and Skills

• Proficiency with the Python deep learning software stack, particularly expertise in PyTorch, Numpy, and related packages.
• Experience handling and processing large and diverse datasets, especially medical texts, journals, or electronic health records.
• Ability to collaborate effectively with non-technical stakeholders, such as doctors and medical researchers.
• Experience with experiment tracking and project management tools, notably frameworks like Weights & Biases.
• Prior experience in fine-tuning large language models for specific tasks.
• Demonstrated experience in optimizing deep learning models for better performance and efficiency.
• Understanding of biology and/or medicine to bridge the gap between pure machine learning and its applications in the medical field.
• A track record of publications in technical conferences or journals.

Additional Information

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.

Please note that we are currently conducting a majority of interviews and onboarding remotely and virtually. We appreciate your understanding.

The Harvard Medical School is not able to provide visa sponsorship for this position.

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Benefits

We invite you to visit Harvard's Total Rewards website (https://hr.harvard.edu/totalrewards) 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
We are committed to cultivating an inclusive workplace culture of faculty, staff, and students with diverse backgrounds, styles, abilities, and motivations. We appreciate and leverage the capabilities, insights, and ideas of all individuals. Harvard Medical School Mission and Community Values

https://hms.harvard.edu/about-hms/campus-culture/mission-community-values-diversity-statement

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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