New York, NY – November 5, 2025 – Columbia University Medical Center is actively recruiting a skilled Machine Learning Engineer to spearhead advancements in Artificial Intelligence within the healthcare sector. The position,located within the Department of Biomedical Informatics,will focus on translating cutting-edge research into practical AI applications.
Advancing Healthcare Through Artificial Intelligence
Table of Contents
- 1. Advancing Healthcare Through Artificial Intelligence
- 2. Key Responsibilities of the Role
- 3. Core Skills and Qualifications
- 4. Compensation and Work Environment
- 5. the Growing Role of AI in Healthcare
- 6. Frequently Asked Questions About Machine Learning in Healthcare
- 7. How have you ensured the interpretability and transparency of machine learning models when presenting findings to clinicians who may not have a strong technical background in healthcare data?
- 8. machine Learning Engineer at Columbia University Medical Center: A Content Creation Role in New York, United States
- 9. Understanding the Unique Intersection of ML and Content in Healthcare
- 10. Core Responsibilities: beyond Model Building
- 11. Required Skills: A Hybrid Profile
- 12. The Columbia University Medical Center Surroundings
- 13. Real-World Example: Predictive Modeling for Sepsis
- 14. Benefits of this Role
- 15. Practical Tips for Applicants
The University’s Department of Biomedical Informatics is at the forefront of innovation, merging healthcare with the transformative power of Artificial Intelligence. This new role is critical for developing, validating, and deploying refined AI models capable of analyzing and interpreting complex biomedical and clinical data. The Engineer will collaborate with a diverse team including clinicians, researchers, software developers, and data scientists.
Key Responsibilities of the Role
The Machine Learning Engineer will oversee the complete AI project lifecycle, from initial data handling to final deployment.Core responsibilities include designing scalable AI solutions, maintaining and refining foundational models-utilizing techniques like in-context learning and prompt engineering-and building robust data pipelines. They will also be responsible for ongoing model monitoring and retraining to maintain accuracy and reliability.
Further duties encompass code progress and validation, report generation, participation in stakeholder meetings and adherence to best practices in documentation and version control. Mentoring junior team members and staying abreast of the latest advancements in AI and healthcare technology are also vital aspects of the position.
Core Skills and Qualifications
Candidates must possess a Master’s degree in Computer Science, informatics, or a related discipline, coupled with at least two years of relevant experience. A PhD is preferred, alongside experience in optimizing training pipelines for large-scale AI models.
| Requirement | Details |
|---|---|
| Education | Master’s (Minimum), PhD (Preferred) in Computer Science or related field |
| experience | 2+ years related experience (Master’s), 1+ years (PhD) |
| Programming Skills | Proficiency in Python, experience with PyTorch, TensorFlow, scikit-learn |
| Cloud Experience | Familiarity with cloud platforms (Azure, Databricks, AWS, GCP) is a plus |
Did You Know? The global healthcare AI market is projected to reach $187.95 billion by 2030, according to a report by Grand View Research, reflecting the growing demand for AI-driven solutions in healthcare.
Compensation and Work Environment
This is a full-time, regular position with a salary range of $175,000 to $200,000 annually. The role offers the potential for flexible and hybrid work arrangements, subject to business needs.Columbia University is an Equal Opportunity/Disability/Veteran Employer and committed to hiring local residents.
Pro Tip: Prepare to discuss your experience with foundational models and your approach to optimizing AI pipelines during the interview process.
the Growing Role of AI in Healthcare
The integration of Artificial Intelligence into healthcare is rapidly transforming the industry. from improving diagnostic accuracy to personalizing treatment plans,AI is poised to revolutionize patient care. Machine Learning Engineers are central to this transformation, requiring expertise in data analysis, model development, and deployment. As AI technologies continue to evolve, the demand for skilled professionals in this field will only continue to grow.
Frequently Asked Questions About Machine Learning in Healthcare
- What is the role of a Machine Learning Engineer in healthcare? A Machine Learning Engineer develops and deploys AI models to analyze medical data, improve diagnoses, and personalize treatment.
- What skills are essential for a Machine Learning Engineer in this field? Strong programming skills in Python and experience with machine learning libraries, plus familiarity with clinical data, are vital.
- What is prompt engineering and why is it important? Prompt engineering is the art of crafting effective instructions for large language models to generate accurate and relevant responses.
- How is Columbia University contributing to advancements in healthcare AI? The University’s Department of Biomedical Informatics is pioneering innovations at the intersection of healthcare and artificial intelligence.
- What is the future outlook for AI in healthcare? The future of healthcare is intrinsically linked to the continued development and implementation of AI technologies.
What are your thoughts on the increasing use of AI in medical diagnoses? Share your comments below, and please share this article with your network!
How have you ensured the interpretability and transparency of machine learning models when presenting findings to clinicians who may not have a strong technical background in healthcare data?
machine Learning Engineer at Columbia University Medical Center: A Content Creation Role in New York, United States
Understanding the Unique Intersection of ML and Content in Healthcare
The role of a Machine Learning Engineer at Columbia University Medical Center (CUMC) isn’t solely about building algorithms; increasingly, it involves a significant content creation component. This is driven by the need to translate complex machine learning models into understandable and actionable insights for clinicians, researchers, and potentially, patients. Located in New York, NY, this position demands a unique skillset bridging technical expertise with dialog proficiency. This article dives deep into what this role entails, the skills required, and what prospective candidates can expect.
Core Responsibilities: beyond Model Building
While core machine learning engineering tasks remain central, the content creation aspect is becoming paramount. Here’s a breakdown:
* Data Visualization & Reporting: transforming raw data and model outputs into compelling visualizations (using tools like Tableau,Power BI,or Python libraries like Matplotlib and Seaborn) is crucial. This isn’t just about charts; its about storytelling with data.
* Documentation & Knowledge Sharing: Creating extensive documentation for models, algorithms, and data pipelines. This includes technical documentation for fellow engineers and user-friendly guides for non-technical stakeholders. Think API documentation, model cards, and internal knowledge base articles.
* Presentation Progress: Preparing and delivering presentations to communicate findings to diverse audiences – from research teams to hospital administrators. This requires translating technical jargon into accessible language.
* Interactive Dashboard Creation: Building interactive dashboards that allow users to explore data and model predictions independently.This empowers users to gain insights without needing direct ML expertise.
* Content for Publications & grants: Assisting in the preparation of content for research publications and grant proposals, often requiring the clear articulation of ML methodologies and results.
Required Skills: A Hybrid Profile
This role demands a blend of technical and soft skills. Here’s a detailed look:
* Technical Skills:
* Programming languages: Python (essential), R (beneficial), SQL.
* Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn.
* Cloud Computing: AWS, Azure, or Google Cloud Platform experience.
* Data engineering: Experience with data pipelines, ETL processes, and data warehousing.
* Big Data Technologies: Spark,Hadoop (depending on the project).
* Content Creation & Communication Skills:
* Data Visualization: Proficiency in tools like Tableau, Power BI, or Python visualization libraries.
* Technical Writing: Ability to write clear, concise, and accurate documentation.
* Presentation Skills: Strong public speaking and presentation skills.
* Storytelling with Data: The ability to translate complex data into compelling narratives.
* Graphic Design Basics: Familiarity with design principles and tools (e.g., Canva, Adobe Illustrator) is a plus.
* Domain Knowledge: A strong understanding of healthcare data, medical terminology, and clinical workflows is highly favorable. Experience with electronic health records (EHR) systems is a significant benefit.
The Columbia University Medical Center Surroundings
CUMC is a leading academic medical center, offering a stimulating environment for machine learning engineers. Expect to work on cutting-edge projects with access to large datasets and collaborations with renowned researchers. The New York City location provides access to a vibrant tech community and numerous networking opportunities. Specifically, CUMC is heavily involved in research areas like:
* Precision Medicine: Utilizing ML to tailor treatments to individual patients.
* Medical Imaging Analysis: Developing algorithms to improve the accuracy and efficiency of image-based diagnostics.
* Predictive analytics: Using ML to predict patient outcomes and identify at-risk individuals.
* Drug Revelation: Applying ML to accelerate the drug development process.
Real-World Example: Predictive Modeling for Sepsis
Consider a project focused on predicting sepsis in ICU patients. A machine learning engineer wouldn’t just build the model; they’d also be responsible for:
- Creating a dashboard displaying real-time sepsis risk scores for each patient.
- Developing a report explaining the model’s performance and limitations to clinicians.
- Presenting the findings to the ICU team, highlighting how the model can aid in early detection and intervention.
- Documenting the entire process for reproducibility and future improvements.
This example illustrates how content creation is integral to the prosperous implementation of ML in a clinical setting.
Benefits of this Role
* Impactful Work: Contribute to advancements in healthcare and improve patient outcomes.
* Intellectual Stimulation: Work on challenging and innovative projects.
* Career Growth: Develop a unique skillset at the intersection of ML and communication.
* Competitive Salary & Benefits: CUMC offers a comprehensive compensation package.
* Location: Enjoy the benefits of living and working in New York city.
Practical Tips for Applicants
* Showcase Your Communication Skills: Include examples of your writing, presentations, or data visualizations in your portfolio.
* Highlight Healthcare Experience: If you have experience working with healthcare data or in a clinical setting, emphasize it.
*
