Machine Learning Engineer, Upstart

• Phd in a quantitative field with 0+ years of relevant experience
• Masters in a quantitative field with 2+years of relevant experience
• Bachelors in a quantitative field with 4+years of relevant experience
• Experience in Python
• Experience with Machine Learning

• Upstart’s Machine Learning, Personal Loan Underwriting team is focused on leveraging machine learning to power Upstart’s underwriting models
• This team is responsible for building the underwriting model which determines credit decisioning at Upstart
• This model plays a central role in Upstart’s overall performance as a company and includes numerous challenging problems to solve
• As a Machine Learning Engineer, PL UW at Upstart, you will collaborate with our Research Scientists, Data Scientists, and other engineering departments
• You will increase ML productivity through better software and infrastructure; and improve how we research, train, deploy, serve and monitor our models
• You will reduce the time it takes to go from idea to production, and enable us to test out new ideas. This will all manifest in us delivering on our critical mission of enabling access to credit more effectively
• You will design and implement code and algorithms to help improve our model’s predictive power
• Build repeatable workflows and automation that enables scientists to spend more time on high leverage tasks (methodology, writing, analysis) and less on keeping the lights on or getting things to run
• Discover and develop a short and long-term roadmap of engineering improvements on the PL UW team
• Explore new algorithms and methodologies for our machine learning models
• Interface cross-functionally with other engineering teams (data engineering, machine learning platform, pricing, and growth software engineering), to provide feedback and requirements, so that we can build high-quality systems end-to-end

• Comprehensive health plans
• 401k Plan
• Life insurance
• Disability insuranc
• Generous vacation policy
• Flexible time off
• Parental leave
• Technology equipment budget
• Personal development budget
• Various clubs and extracurricular activities
• Catered lunches
• Fully-stocked micro-kitchen

Total funding: $135.7m

Business startups across the world usually seek out funding, but this is often difficult for companies led by millenials. Millennial entrepreneurs with an excellent track record in business are increasingly requiring access to these loans, but their lack of credit history results in proposals frequently being declined.