Let's begin! Machine Learning Engineer

Moody's•Chicago, CO

1d•Onsite

About The Position

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence. If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. Employment eligibility to work in the U.S. is required, as Moody’s will not pursue visa sponsorship for this position. Our Machine Learning Technology team is responsible for the core technology behind award-winning property intelligence solutions. We leverage machine learning, geospatial imagery, and computer vision to measure and monitor the built environment while delivering actionable insights to clients. By joining our team, you will contribute to innovative work that helps organizations better understand how homes and workplaces can withstand evolving climate and economic risks, while advancing the responsible adoption of artificial intelligence across our products and solutions.

Requirements

Expertise in Python programming, including machine learning libraries such as NumPy, Pandas, and PyTorch
Experience with machine learning operations practices, including continuous integration and continuous deployment pipelines, model monitoring, and model maintenance preferred
Expertise with modern machine learning tools and platforms, including Jupyter, Docker, Git, and cloud computing environments such as Amazon Web Services or Google Cloud Platform
Experience building data tools for extract, transform, and load processes, extracting data from SQL and NoSQL databases, and conducting advanced data analysis
Experience with geographic information systems preferred
Excellent written and verbal communication skills, with the ability to understand and articulate business requirements and objectives to both technical and non-technical stakeholders
Expertise in supervised and unsupervised machine learning algorithms and their implementations, including advanced concepts such as active learning, computer vision, and deployments in complex environments
Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency.
Strong experience using AI tools to lead innovation initiatives.
Demonstrated leadership in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization
Advanced degree in a Science, Technology, Engineering, or Mathematics field required
Typically requires a minimum of two years of hands-on industry experience in machine learning, computer vision, data science, or a related field

Nice To Haves

Experience with machine learning operations practices, including continuous integration and continuous deployment pipelines, model monitoring, and model maintenance
Experience with geographic information systems

Responsibilities

Develop practical, scalable, and robust machine learning and computer vision solutions that enhance product capabilities and deliver value to clients.
Collect, clean, preprocess, and analyze data to support machine learning model development and maximize data value
Create visualizations and conduct exploratory analysis to identify patterns, trends, opportunities, and data quality issues
Train, evaluate, refine, and deploy machine learning models aligned with business objectives and product requirements
Design, implement, and automate large-scale model training, integration, and evaluation pipelines
Collaborate with machine learning, software engineering, product development, sales, and cross-functional stakeholders to deliver innovative solutions
Communicate technical findings and recommendations to both technical and non-technical audiences through clear documentation and presentations
Design and execute experiments to validate assumptions, improve model performance, and support data-driven decision making
Ensure projects follow governance, security, ethical AI, and responsible data use practices while identifying and resolving operational inefficiencies