Columbia University
Job Type: Officer of Administration
Bargaining Unit:
Regular/Temporary: Regular
End Date if Temporary:
Hours Per Week: 35
Standard Work Schedule: Monday - Friday
Building: PH-20
Salary Range: $175,000.00 - $200,000.00
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.
Position Summary
Columbia University’s Department of Biomedical Informatics is advancing innovation at the intersection of healthcare and artificial intelligence. We are seeking a highly skilled and motivated Machine Learning Engineer with expertise in generative AI. The successful candidate will play a central role in developing, validating, and deploying advanced AI models to analyze and interpret complex biomedical and clinical data. This position involves collaborating with a multi-disciplinary team of clinicians, academic researchers, software engineers, and informaticians to translate research into scalable, production-ready AI solutions that improve diagnosis, treatment planning, and patient care. Responsibilities
Collaborate with cross-functional teams to analyze biomedical and clinical datasets—including free text, structured data, and multi-modal inputs—and design and implement scalable AI solutions. Maintain, fine-tune, and validate foundational models for clinical data, applying methods such as in-context learning, prompt engineering, and instruction tuning. Develop robust data pipelines for data ingestion, cleaning, and preprocessing. Build, evaluate, and optimize AI/ML models (foundation-based or bespoke) tailored to specific healthcare tasks. Develop and maintain machine learning pipelines for production deployment. Contribute to continuous monitoring of model performance and support model retraining to ensure accuracy, fairness, and clinical reliability. Write, validate, and execute code across local and cloud-based environments (CPU/GPU). Create clear and informative reports and visualizations to summarize data, results, and performance metrics. Actively participate in stakeholder meetings, presentations, and discussions. Follow best practices in documentation, code repositories, containers/environments, and version control. Mentor junior engineers and data scientists as appropriate. Stay abreast of emerging methods and tools in AI, NLP, and healthcare technology to ensure Columbia’s solutions remain cutting edge. Perform other related duties and special projects as assigned. Minimum Qualifications
Master's degree in computer science, informatics or related field, and/or equivalent in education and experience, with at least 2 years’ related experience. Preferred Qualifications
PhD degree in computer science, informatics or related field, and/or equivalent and experience in education, with at least 1 year of related work experience. Other Requirements
Experience working with foundational models, prompt engineering, instruction tuning, in-context learning. Experience optimizing training pipelines for scaling novel foundation model architectures to extreme datasets and model sizes over appropriate compute resources. Strong programming skills in Python and experience with machine learning libraries (e.g., PyTorch, TensorFlow, scikit-learn) as well as visualization packages. Experience with cloud platforms (e.g., Azure, Databricks, AWS, GCP) is a plus. Experience with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) pipelines. Experience working with clinical data is a plus. Excellent analytical and problem-solving skills. Strong communication and collaboration skills. Equal Opportunity Employer / Disability / Veteran
Columbia University is committed to the hiring of qualified local residents. Subject to business needs, we may support flexible and hybrid work arrangements. Options will be discussed during the interview process.
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Columbia University’s Department of Biomedical Informatics is advancing innovation at the intersection of healthcare and artificial intelligence. We are seeking a highly skilled and motivated Machine Learning Engineer with expertise in generative AI. The successful candidate will play a central role in developing, validating, and deploying advanced AI models to analyze and interpret complex biomedical and clinical data. This position involves collaborating with a multi-disciplinary team of clinicians, academic researchers, software engineers, and informaticians to translate research into scalable, production-ready AI solutions that improve diagnosis, treatment planning, and patient care. Responsibilities
Collaborate with cross-functional teams to analyze biomedical and clinical datasets—including free text, structured data, and multi-modal inputs—and design and implement scalable AI solutions. Maintain, fine-tune, and validate foundational models for clinical data, applying methods such as in-context learning, prompt engineering, and instruction tuning. Develop robust data pipelines for data ingestion, cleaning, and preprocessing. Build, evaluate, and optimize AI/ML models (foundation-based or bespoke) tailored to specific healthcare tasks. Develop and maintain machine learning pipelines for production deployment. Contribute to continuous monitoring of model performance and support model retraining to ensure accuracy, fairness, and clinical reliability. Write, validate, and execute code across local and cloud-based environments (CPU/GPU). Create clear and informative reports and visualizations to summarize data, results, and performance metrics. Actively participate in stakeholder meetings, presentations, and discussions. Follow best practices in documentation, code repositories, containers/environments, and version control. Mentor junior engineers and data scientists as appropriate. Stay abreast of emerging methods and tools in AI, NLP, and healthcare technology to ensure Columbia’s solutions remain cutting edge. Perform other related duties and special projects as assigned. Minimum Qualifications
Master's degree in computer science, informatics or related field, and/or equivalent in education and experience, with at least 2 years’ related experience. Preferred Qualifications
PhD degree in computer science, informatics or related field, and/or equivalent and experience in education, with at least 1 year of related work experience. Other Requirements
Experience working with foundational models, prompt engineering, instruction tuning, in-context learning. Experience optimizing training pipelines for scaling novel foundation model architectures to extreme datasets and model sizes over appropriate compute resources. Strong programming skills in Python and experience with machine learning libraries (e.g., PyTorch, TensorFlow, scikit-learn) as well as visualization packages. Experience with cloud platforms (e.g., Azure, Databricks, AWS, GCP) is a plus. Experience with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) pipelines. Experience working with clinical data is a plus. Excellent analytical and problem-solving skills. Strong communication and collaboration skills. Equal Opportunity Employer / Disability / Veteran
Columbia University is committed to the hiring of qualified local residents. Subject to business needs, we may support flexible and hybrid work arrangements. Options will be discussed during the interview process.
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