Machine Learning Engineer

Harvard Medical School’s Core for Computational Biomedicine (CCB) is seeking a Machine Learning Engineer with advanced expertise to help develop and optimize medical large language models for medical education and clinical decision support.

Role Overview

The position will focus on building and improving medical LLMs, translating model outputs into practical tools through APIs, and coordinating with cross-disciplinary teams to align technical development with domain-specific requirements.

Key Responsibilities

• Develop, implement, and optimize medical large language models designed for medical education and clinical decision support.
• Work with interdisciplinary teams that include biologists, clinicians, and data scientists to identify domain-specific needs and convert them into computational solutions.
• Monitor and apply the latest advancements in deep learning and machine learning to keep developed models state-of-the-art.
• Create infrastructure for data transformation and ingestion.
• Build AI models capable of making predictions using large volumes of data.
• Communicate the usefulness of the AI models to relevant stakeholders.
• Transform machine learning models into APIs so they can be integrated with other applications.
• Use 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.
• Proficiency with the Python deep learning stack, including expertise in PyTorch, NumPy, and related packages.
• Experience handling and processing large and diverse datasets, with emphasis on medical texts, journals, or electronic health records.
• Ability to collaborate effectively with non-technical stakeholders, including doctors and medical researchers.
• Experience with experiment tracking and project management tools, including Weights & Biases.
• Experience fine-tuning large language models for specific tasks.
• Demonstrated ability to optimize deep learning models for improved performance and efficiency.
• Understanding of biology and/or medicine to connect machine learning methods to medical applications.
• Track record of publications in technical conferences or journals.
• A Master’s or PhD in Computer Science, Computational Biology, or a related field is strongly preferred.

Technology Stack

• 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 not working at 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 work details will be discussed during the interview process.

Additional Information

• Visa Sponsorship: Harvard University is unable to provide visa sponsorship for this position.
• Pre-Employment Screening: Identity, Education, Criminal
• Interview Process: Majority of interviews and onboarding are currently conducted remotely and virtually.
• Application Status: Applicants may track their status through the Careers@Harvard portal.

Salary Grade

This position is salary grade level 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

Education Requirement

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

Hiring Note

Certain visa types and funding sources may limit work location, and individuals must meet work location sponsorship requirements prior to employment.