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Mount Sinai Health System

Machine Learning Engineer I - Windreich Department of Artificial Intelligence & Human Health (On Site)

On site

New york, United states

$ 163,695 /year

Junior

Full Time

04-10-2025

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Skills

Leadership Python Unity SQL Big Data Data Engineering CI/CD DevOps Docker Kubernetes Monitoring Version Control Problem-solving Decision-making Research Architecture Machine Learning git benchmarking Organization Azure AWS GCP Hadoop Spark CI/CD Pipelines Mathematics

Job Specifications

Description

We are seeking a skilled Machine Learning Engineer I to join our team in the SinAI Assurance Lab. The Machine Learning Engineer will play a key role in Machine Learning Operations and will be for designing, maintaining, and optimizing data infrastructure and model validation pipelines that ensure all AI systems, Generative and Non-Generative, deployed across the Mount Sinai Health System (MSHS) are rigorously validated for compliance, performance, and patient safety.

You will work closely with AI product teams, clinical and technical stakeholders, DevOps engineers, and the AI Governance Committee to engineer scalable data flows that support model validation, real-time monitoring, and simulation-based testing environments.

Responsibilities

General Data Engineering

Build and maintain robust ETL pipelines for structured and unstructured clinical data from EHR, imaging, and text sources.
Design systems to automate data preparation, lineage tracking, and reproducibility for AI model inputs and outputs.
Develop data infrastructure for benchmarking and stress-testing models in clinical simulation environments.
Collaborate with DevOps and cloud teams to ensure deployment pipelines meet compliance and performance standards.
Set up and monitor model tracking infrastructure for evaluation metrics and drift detection.
Assist in the development of standards and procedures affecting data management, design and maintenance. Documents all standards and procedures.

AI Assurance & Governance

Engineer and maintain pipelines that support pre-deployment model validation and post-deployment monitoring.
Collaborate with Data Scientists and Clinical Product Owners to validate data integrity, reproducibility, and fairness in AI workflows.
Ensure compliance with HIPAA, ethical guidelines, and institutional governance policies on sensitive health data use.
Build dashboards and tools that provide observability across the ML lifecycle: data, models, outcomes.

Stakeholder Engagement & Others

Effective communicate technical findings related to model and data integrity to governance teams, clinical stakeholders, and leadership.
Maintain clear and well-organized documentation of data workflows, platform architecture, and validation processes.
Help write internal reports on data infrastructure resilience, validation system status, and operational risk.
Stay informed on industry best practices in data engineering and healthcare-focused machine learning.
Possess an extremely flexible attitude. Willing to work with multiple types of technologies and languages with an open mind and without technology bias. Continuous interest in updating skill sets and knowledge of trends in the Big Data Technology space.
Work closely with cross-functional teams including data scientists, healthcare providers, and IT professionals to understand data requirements, develop solutions, and support data-driven decision-making.
Other duties as assigned

Qualifications

Requirements

Bachelor's degree in Computer Science, Statistics, Mathematics, or related field; Master's degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Information Technology, Mathematics, Physics) is preferred.
2+ years of experience in data engineering, software engineering, or machine learning.
Proficient in Python and SQL
Proficiency in at least one cloud computing platforms (e.g., AWS, Azure, GCP)
Intermediate knowledge of Machine Learning
Familiarity with ML lifecycle management tools (e.g., MLflow, Kubeflow, Airflow)
Experience on deployment and operationalization of ML Systems
Experience with monitoring tools for AI model tracking
Understanding of DevOps principles, CI/CD pipelines, and containerization (e.g., Docker, Kubernetes)
Experience with version control systems (e.g., Git) Knowledge of big data technologies (e.g., Hadoop, Spark)
Strong problem-solving skills and ability to work in cross-functional teams

Employer Description

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 cult

About the Company

The Mount Sinai Health System is an integrated health system committed to providing distinguished care, conducting transformative research, and advancing biomedical education. Structured around seven hospital campuses and a single medical school, the Health System has an extensive ambulatory network and a range of inpatient and outpatient services--from community-based facilities to tertiary and quaternary care. WHO WE ARE We are compassionate collaborators--48,000 strong--working to heal, teach, and advance medicine in New ... Know more