Senior ML Backend Engineer

At the company, 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.

• Experience designing, building, and maintaining machine learning infrastructure, platforms, and tooling that support large-scale model training, evaluation, and deployment
• Proficiency in Python and modern machine learning engineering tools, including deep learning frameworks, experiment tracking platforms, containerisation, version control, and automated workflows
• Experience with machine learning operations practices, including continuous integration and deployment, model monitoring, reproducibility, data lineage, and model governance
• Proven expertise working with cloud-native technologies, Kubernetes, distributed computing environments, and scalable infrastructure supporting machine learning workloads
• Strong analytical and problem-solving skills, with the ability to evaluate technical trade-offs, support model development teams, and communicate complex concepts to both technical and non-technical stakeholders
• Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using artificial intelligence tools, including coding assistants and large language model-based agents, to improve engineering productivity, automate workflows, and enhance operational efficiency
• Proven ability to implement artificial intelligence-powered solutions to solve business challenges, with a commitment to responsible and ethical AI practices, including model evaluation, governance, risk awareness, and documentation
• Successful candidates will be senior backend engineers with a good understanding of ML and a strong interest to go deeper on ML

• PhD in a science, technology, engineering, or mathematics discipline preferred
• Master's degree with significant relevant industry experience or Bachelor's degree with extensive hands‑on industry experience considered
• Equivalent practical experience and non‑traditional career paths will also be considered

Build and evolve machine learning infrastructure that enables scalable development, deployment, and monitoring of advanced property intelligence models

• Maintain and enhance shared machine learning training, evaluation, and deployment platforms used across the engineering organisation
• Design and implement reliable, scalable, and cost‑effective machine learning infrastructure, pipelines, and tooling
• Partner with machine learning engineers and researchers to transition new modelling approaches from prototype to production
• Develop and improve automation, observability, monitoring, testing, and governance capabilities across machine learning systems
• Evaluate and onboard new data sources, platforms, and technologies that improve model performance and operational efficiency
• Collaborate with software engineering, technology, product, and commercial teams to deliver scalable machine learning solutions
• Ensure solutions align with security, governance, responsible AI, and model risk management requirements

Our Machine Learning Technology team is responsible for the core platforms, infrastructure, and engineering capabilities that power the company industry‑leading property intelligence solutions. We combine machine learning, computer vision, geospatial analytics, and artificial intelligence to generate insights from large-scale aerial and satellite imagery. Our team develops and operates the platforms that enable rapid experimentation, scalable model training, production deployment, and continuous model improvement.

We also leverage AI-enabled engineering…