Machine Learning Engineer

Company Description

By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.

Harvard Medical School's mission is to nurture a diverse, inclusive community dedicated to alleviating suffering and improving health and well‑being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership.

You’ll be at the heart of biomedical discovery, education, and innovation, working alongside world‑renowned faculty and a community dedicated to improving human health. This is more than a job - it’s an opportunity to shape the future of medicine.

The Core for Computational Biomedicine (CCB) in the Department of Biomedical Informatics (DBMI) at Harvard Medical School (HMS) is looking for a Machine Learning Engineer with advanced expertise to lead development of 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.

• 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.
• Use expert knowledge to lead research AI and data science projects.

• Minimum of seven years’ post‑secondary education or relevant work experience.

• A Master's or PhD in Computer Science, Computational Biology, or a related field is strongly preferred.
• Minimum of 3 years of hands‑on experience in developing complex deep learning solutions to tackle scientific challenges.
• 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.

• Standard Hours/
  
  Schedule:
  
  35 hours per week
• Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position.
• Pre‑Employment Screening: Identity, Education, Criminal
• Other Information: Please note that we are currently conducting a majority of interviews and…