Mount Sinai Health System
Machine Learning Engineer I - Multimodal Artificial Intelligence for Women's Hea
Mount Sinai Health System, New York, New York, us, 10261
Machine Learning Engineer I - Multimodal Artificial Intelligence for Women’s Health (On Site)
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.
Heavy menstrual bleeding affects nearly one in three women of reproductive age and is a leading cause of iron deficiency worldwide. Our lab is dedicated to addressing this challenge and has been awarded a Wellcome Leap Missed Vital Sign grant to drive research and translation.
As a founding member, you will 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, the Minerva HPC cluster, and eHive, a digital platform for wearable and real‑world data collection, along with international collaboration opportunities.
Responsibilities
Build, train and evaluate machine learning models on large scale multimodal datasets (wearables, imaging, genomics, EHR)
Develop and maintain reproducible, scalable machine learning 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. Track and report progress. Keep stakeholders apprised of project status and implications for completion
Prepare clear, well‑organized project‑specific documentation, including methods used, key decision points and caveats, with sufficient detail to support comprehension and replication
Share development and process knowledge with other analysts to assure redundancy and continuously build 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
Work 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
Demonstrate advanced level proficiency with the principles and methodologies of process improvement. Apply these in the execution of responsibilities in support of a process focused approach
Other duties as assigned
Qualifications
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 (Hadoop, Spark)
Knowledge of Software Development Lifecycle
Self‑motivated with demonstrated ability to work independently and exercise independent judgment in developing complex techniques or programs in a dynamic environment
Understanding of machine learning algorithms (Supervised, Unsupervised ML algorithms)
Familiarity with SQL or other database languages
#J-18808-Ljbffr
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.
Heavy menstrual bleeding affects nearly one in three women of reproductive age and is a leading cause of iron deficiency worldwide. Our lab is dedicated to addressing this challenge and has been awarded a Wellcome Leap Missed Vital Sign grant to drive research and translation.
As a founding member, you will 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, the Minerva HPC cluster, and eHive, a digital platform for wearable and real‑world data collection, along with international collaboration opportunities.
Responsibilities
Build, train and evaluate machine learning models on large scale multimodal datasets (wearables, imaging, genomics, EHR)
Develop and maintain reproducible, scalable machine learning 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. Track and report progress. Keep stakeholders apprised of project status and implications for completion
Prepare clear, well‑organized project‑specific documentation, including methods used, key decision points and caveats, with sufficient detail to support comprehension and replication
Share development and process knowledge with other analysts to assure redundancy and continuously build 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
Work 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
Demonstrate advanced level proficiency with the principles and methodologies of process improvement. Apply these in the execution of responsibilities in support of a process focused approach
Other duties as assigned
Qualifications
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 (Hadoop, Spark)
Knowledge of Software Development Lifecycle
Self‑motivated with demonstrated ability to work independently and exercise independent judgment in developing complex techniques or programs in a dynamic environment
Understanding of machine learning algorithms (Supervised, Unsupervised ML algorithms)
Familiarity with SQL or other database languages
#J-18808-Ljbffr