Machine Learning Engineer

Harvard Medical School’s Core for Computational Biomedicine (CCB) is seeking a Machine Learning Engineer to lead the development of medical large language models (LLMs). The role emphasizes building and optimizing LLMs for medical education and clinical decision support, including integration into application workflows through APIs and cross-disciplinary collaboration.

Key Responsibilities

• Develop, implement, and optimize medical large language models designed for medical education and clinical decision support.
• Collaborate with interdisciplinary teams that include biologists, clinicians, and data scientists to capture domain requirements and translate them into computational solutions.
• Monitor advancements in deep learning and machine learning to ensure the developed models remain state-of-the-art.
• Build infrastructure for data transformation and ingestion.
• Create AI models that generate predictions using large volumes of data.
• Communicate the usefulness of developed AI models to stakeholders.
• Transform machine learning models into APIs to enable interaction with other applications.
• Apply expert knowledge to lead research AI and data science projects.

Required Qualifications

• Minimum of seven years of post-secondary education or relevant work experience.
• Minimum of 3 years of hands-on experience developing complex deep learning solutions to address scientific challenges.
• Strong proficiency in the Python deep learning stack, with expertise in PyTorch, Numpy, and related packages.
• Experience working with 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, including Weights & Biases.
• Prior experience fine-tuning large language models for specific tasks.
• Demonstrated experience optimizing deep learning models for improved performance and efficiency.
• Understanding of biology and/or medicine to connect machine learning approaches with medical applications.
• Track record of publications in technical conferences or journals.

Preferred Education

A Master’s or PhD in Computer Science, Computational Biology, or a related field is strongly preferred.

Technologies

• Python
• PyTorch
• Numpy
• Weights & Biases

Work Location and Schedule

• Location: Boston, MA (hybrid)
• Standard Hours/Schedule: 35 hours per week
• This role has been determined by school or unit leaders to allow some duties to be performed at a non-Harvard location.
• The work schedule and location will be set by the department based on operational needs.
• When working outside a Harvard or Harvard-designated location, hybrid employees must work in a Harvard registered state in compliance with the University’s Policy on Employment Outside of Massachusetts.
• Additional details will be discussed during the interview process.
• Certain visa types and funding sources may limit work location.
• Individuals must meet work location sponsorship requirements prior to employment.

Additional Information

• Visa Sponsorship: Harvard University is unable to provide visa sponsorship for this position.
• Pre-Employment Screening: Identity, Education, Criminal
• Interview/Onboarding: Majority of interviews and onboarding are currently conducted remotely and virtually.
• Application Status: Due to high application volume, responses may not be immediate; candidates can track status via the Careers@Harvard portal.

Salary Grade

This position is salary grade 060.

Benefits

• Generous paid time off including parental leave
• Medical, dental, and vision health insurance coverage starting on day one
• Retirement plans with university contributions
• Wellbeing and mental health resources
• Support for families and caregivers
• Professional development opportunities including tuition assistance and reimbursement
• Commuter benefits, discounts and campus perks

Harvard offers a comprehensive benefits package designed to support a healthy work-life balance and your physical, mental, and financial wellbeing.