Senior Software Engineer, Machine Learning Platform

About Upstart

Upstart is a leading AI lending marketplace partnering with banks and credit unions to expand access to affordable credit. By leveraging Upstart's AI marketplace, Upstart-powered banks and credit unions can have higher approval rates and lower loss rates across races, ages, and genders, while simultaneously delivering the exceptional digital-first lending experience their customers demand. More than two-thirds of Upstart loans are approved instantly and are fully automated.

Upstart is a digital-first company, which means that most Upstarters live and work anywhere in the United States. However, we also have offices in San Mateo, California; Columbus, Ohio; and Austin, Texas.

Most Upstarters join us because they connect with our mission of enabling access to effortless credit based on true risk. If you are energized by the impact you can make at Upstart, we’d love to hear from you!

The Team: 

Upstart’s ML Platform team is the engineering team responsible for designing and developing the technology and tools that underpin Upstart’s machine learning capabilities.The team is focused on supporting Upstart’s ML teams in converting research ideas to production models.

As a Senior Software Engineer on the ML Platform team, you’ll be responsible for building an MLOps platform to support machine learning training, process automation, model deployment, and monitoring. Machine Learning is critical to Upstart’s core business, and our greatest competitive advantage lies in the fact that we’re able to innovate on our AI engine quickly.

Position Location - This role is available in the following locations: Remote, San Mateo, Columbus, Austin

Time Zone Requirements - This team operates on the East/West Coast time zones.

Travel Requirements - This team has regular on-site collaboration sessions. These occur 3 days per quarter at an Upstart office. If you need to travel to make these meetups, Upstart will cover all travel related expenses.

How you’ll make an impact:

• Build, maintain, and optimize high-quality software applications that enable machine learning models to be applied to the ever-evolving needs of the business
• Develop the scaffolding, tooling, and infrastructure that allows research scientists to iterate and train large scale machine learning models
• Enable rapid experimentation and iteration in model training code so that we can continuously improve our models
• Build the infrastructure that provides research scientists the ability to access the data and features they need to …