Machine Learning Engineer I - Multimodal Artificial Intelligence for Women's Health

Machine Learning Engineer I will be primarily responsible for contributing to the development and enhancement of machine learning applications and systems. They will work closely with other engineers and data scientists to design and implement scalable and efficient machine learning systems.

We are recruiting a Machine Learning Engineer I to support the lab’s core projects in multimodal AI for women’s health. The engineer will be responsible for building, optimizing, and deploying ML pipelines at scale, working with both postdocs and clinicians. This role is ideal for an applied researcher who is excited about translational machine learning and thrives in a collaborative, interdisciplinary environment.

Heavy menstrual bleeding affects nearly one in three women of reproductive age and is a leading cause of iron deficiency worldwide. Yet it remains one of the most under-recognized challenges in medicine. Our lab at the intersection between the Artificial Intelligence and Human Health Department and the Department for Obstetrics, Gynecology and Reproductive Sciences at Mount Sinai has been awarded a Wellcome Leap Missed Vital Sign grant to change this.

We are building a new, interdisciplinary group at the intersection of AI, human health, and obstetrics & gynecology. Our mission is to harness state-of-the-art methods in machine learning and multimodal data integration to close critical gaps in women’s health—and to translate these advances into solutions that matter for patients and clinicians.

As a founding member, you will help shape a lab designed for openness, collaboration, and translation. You will have access to unique resources including Mount Sinai’s genome-linked EHR biobank (the Sinai Million), AIRMS (AI-ready Mount Sinai Integrated Data and Analytics Platform), the Minerva HPC cluster, and eHive, a digital platform for wearable and real-world data collection. Partnerships with the Hasso Plattner Institute in Germany create further opportunities for international collaboration.

This is a chance to join at the ground level of a lab committed to impact: bringing computational innovation directly into women’s health.

• Build, train and evaluate machine learning models on large scale multimodal datasets (wearables, imaging, genomics, EHR)
• Develop and maintain reproductible, scalable ML pipelines using PyTorch
• Run experiments on HPC clusters (Minerva) and support distributed learning (e.g. Accelerate, Lightning)
• Optimize workflows for compute and data efficiency
• Collaborate with post-doctoral fellows and clinical researchers to translate models into practice
• Contribute to codebases, documentation and open source tools
• Assist in the collection, cleaning, and curation of large data sets.
• Assist in the operationalization of machine learning models.
• Participate in evaluating model performance and contribute to model refinement.
• Work with other team members to deploy machine learning models.
• Contribute to maintaining clear and organized documentation of machine learning systems.
• Stay updated with the latest trends and technologies in the machine learning field.
• Work collaboratively with a multidisciplinary team to ensure the effectiveness of machine learning systems.
• Develop and maintain project work plans, including critical tasks, milestones, timelines, interdependencies, and contingencies.  Tracks and reports progress.  Keeps stakeholders apprised of project status and implications for completion. 
• Prepare clear, well-organized project-specific documentation, including, at a minimum, analytic methods used, key decision points and caveats, with sufficient detail to support comprehension and replication. 
• Share development and process knowledge with other analysts in order to assure redundancy and continuously builds a core of analytical strength within the organization.
• Adhere to corporate standards for performance metrics, data collection, data integrity, query design, and reporting format to ensure high quality, meaningful analytic output.
• Works closely with IT on the ongoing improvement of Mount Sinai’s integrated data warehouse, driven by strategic and business needs, and designed to ensure data and reporting consistency throughout the organization.
• Demonstrates advanced level proficiency with the principles and methodologies of process improvement. Applies these in the execution of responsibilities in support of a process focused approach.
• Other duties as assigned.

Requirements

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Biomedical Informatics or related field.
• Experience in applied machine learning and deep learning using PyTorch
• Experience in  HPC environments, distributed training, and large scale data processing
• Familiarity with version control, containerization (Docker, Singularity) and reproducible research practices
• Experience with clinical data and biomedical informatics (OMOP, FHIR) preferred
• Background in multi-modal Machine Learning, time series analysis, or computer vision preferred
• Azure Cloud experience preferred
• Interest in translational applications in Women's Health preferred
• Knowledge of at least one programming language among Scala, Python, Java, C, or C++.
• Knowledge of big data technologies (e.g., Hadoop, Spark)
• Knowledge of Software Development Lifecycle.
• Self-motivated with a demonstrated ability to work independently, and to exercise independent judgment in developing complex techniques or programs in a dynamic environment.
• Act as the major contributor in the development and operationalization of four different applications.
• Play a key technical role in maintaining deployed products
• Understanding of machine learning algorithms (Supervised, Unsupervised ML algorithms).
• Familiarity with SQL or other database languages.
• Act as the major contributor in the development and operationalization of four different applications.
• Play a key technical role in maintaining deployed products

Strength through Unity and Inclusion

The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai’s unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual.

At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history.

About the Mount Sinai Health System:

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, more than 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time — discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients’ medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high "Honor Roll" status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Report’s “Best Children’s Hospitals” ranks Mount Sinai Kravis Children's Hospital among the country’s best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek’s “The World’s Best Smart Hospitals” ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.

Equal Opportunity Employer

The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization.