The Team

As a Software Engineer on the Machine Learning team, you'll join an exciting new team focused on providing engineering solutions to enable our research scientists and ML engineers to more effectively realize their value to the business. 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. This team’s mission is to solve software problems in the product that consume our ML models and build infrastructure to allow research scientists to move faster with respect to model training, development, testing, and deployment. 

• Position Location - This role is available in the following locations: Columbus, Austin or Remote ^(#LI-REMOTE) 
• Time Zone Requirements - This team operates on either the East or West Coast time zones.
• Travel Requirements - This team has regular on-site collaboration sessions six times annually split between the San Mateo or Columbus office locations. 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 monitor high-quality software applications that enable machine learning models to be applied to the ever-evolving needs of the business
• Develop the scaffolding that allows research scientists to integrate models into production services and expose APIs to allow other software services to leverage them
• Enable rapid experimentation and feedback loops in our model serving code so that we have an ability to continuously improve
• Work collaboratively with research scientists, data engineers, and devops engineers to ensure that model training and deployment is a repeatable process that relies heavily on robots (not humans)

What we're looking for:

• 3+ years of software engineering experience
• Experience building and maintaining backend software services and APIs
• Familiarity with containers, continuous integration, and kubernetes
• Experience with some or many of the following: python (flask/django), ruby on rails, scikit-learn, interactive notebooks (jupyter/zeppelin), apache spark, kafka, and AWS
• Excellent quantitative reasoning skills
• Interest in working at the intersection of engineering and machine learning
• Desire to be part of a dynamic and fast growing team