Machine Learning Engineer

Upstart•San Francisco, CA

6d•Remote

About The Position

About Upstart Upstart is the 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 80% of borrowers are approved instantly, with zero documentation to upload. 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; Austin, Texas; and New York City, NY (opening Summer 2026). 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 Teams: Personal Loan, Underwriting: This 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. Your work will impact the team in a number of ways: 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 Growth Team: Upstart’s Growth Team engages in marketing and borrower acquisition through direct mail (DM) campaigns and targeted loan offers on partner sites, leveraging machine learning to drive growth. The Growth ML team develops models to optimize borrower targeting, predict conversion probabilities, and refine pricing strategies across these channels. Our team has an outsized impact on Upstart’s success - the majority of the loans we originate can be directly attributed to the growth team’s efforts. The Growth ML organization is currently growing rapidly and expanding our scope into new problem spaces.

Requirements

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

Responsibilities

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

Benefits

Competitive Compensation (base + bonus & equity)
Comprehensive medical, dental, and vision coverage with Health Savings Account contributions from Upstart
401(k) with 100% company match up to $4,500 and immediate vesting and after-tax savings
Employee Stock Purchase Plan (ESPP)
Life and disability insurance
Generous holiday, vacation, sick and safety leave
Supportive parental, family care, and military leave programs
Annual wellness, technology & ergonomic reimbursement programs
Social activities including team events and onsites, all-company updates, employee resource groups (ERGs), and other interest groups such as book clubs, fitness, investing, and volunteering
Catered lunches + snacks & drinks when working in offices