Machine Learning / Generative AI Engineer

At Iron Mountain we know that work, when done well, makes a positive impact for our customers, our employees, and our planet. That’s why we need smart, committed people to join us. Whether you’re looking to start your career or make a change, talk to us and see how you can elevate the power of your work at Iron Mountain.

We provide expert, sustainable solutions in records and information management, digital transformation services, data centers, asset lifecycle management, and fine art storage, handling, and logistics. We proudly partner every day with our 225,000 customers around the world to preserve their invaluable artifacts, extract more from their inventory, and protect their data privacy in innovative and socially responsible ways. 

Are you curious about being part of our growth stor​y while evolving your skills in a culture that will welcome your unique contributions? If so, let's start the conversation.

• Build and testmachinelearningand deeplearningmodels, report experiment comparisons
• Develop robustmachinelearningpipelines to support production processes
• Process and transform source data to be used inmachinelearningpipelines, leveraging distributed computing environments and parallel processing approaches
• Develop robust evaluation modules
• Conduct statistical analysis and create data visualizations
• Design and plan experiments
• Runmachinelearningexperiments in distributed computing environments, leveraging parallel processing approaches
• Communicate findings with business stakeholders and collaborate to develop solutions that meet customer needs
• Develop code and model documentation and assist with model governance approvals
• Design, develop, and deploy scalablemachinelearningmodels and systems that support business objectives.
• Collaborate with data scientists and dataengineersto collect and preprocess data, and build and maintain data pipelines.
• Develop and maintain data infrastructures that supportmachinelearningworkflows, including data storage, featureengineering, and model training.
• Design and implement distributed systems that support large-scalemachinelearning.
• Develop and maintainmachinelearningworkflows that are efficient, reproducible, and scalable.
• Implement monitoring and evaluation systems that track model performance and identify potential issues.
• Use data visualization tools to communicate insights and findings to stakeholders.
• Background in PromptEngineering