Machine Learning Engineer Intern

Upstart • San Mateo, CA

14d • 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; 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 Machine Learning is at the heart of Upstart’s business model, our models are the product. Our team includes research scientists, data scientists, and machine learning engineers who build and improve production models and the systems around them across the funnel: underwriting and pricing, fraud detection, performance marketing, loan servicing and fair lending/explainability. We tackle high‑impact problems, from underwriting and pricing to monitoring and fairness, where creativity, rigor, and strong engineering directly move the business. The Role As a Machine Learning Engineering Intern at Upstart, you’ll build tools and production‑grade code that make our models better and our researchers faster (think MLE: Code} → Better Code}). You will implement algorithmic improvements that boost predictive power, training efficiency, or serving latency; design reliable data/feature pipelines; and automate repeatable workflows that reduce time from research to deployment and monitoring. You’ll receive mentorship from experienced ML practitioners and collaborate closely with ML scientists to deliver solutions to real‑world engineering problems.

Requirements

• Strong academic credentials with an ongoing bachelor’s or master’s degree in computer science, physics, machine learning, or other quantitative areas of study.
• We require that you are on track to graduate by the summer of 2027 (our internship program is not open to first- and second-year undergraduates).
• Programming skills in Python.
• Proficiency across machine learning, numerical computing, and software engineering fundamentals.
• Foundations in probability and statistics.
• Strong sense of intellectual curiosity, humility, drive and teamwork, as well as communication skills.

Nice To Haves

• PhD studies or post-doctoral research in computer science, physics, machine learning, or other another quantitative field.
• If you are a PhD student, we require that you are on track to graduate by the summer of 2027.
• Experience building models and conducting statistical or quantitative research.
• Experience building ML tooling or solving real‑world ML engineering problems in industry (e.g., data pipelines, evaluation frameworks, training/serving), such as through prior internships.

Responsibilities

• Deliver projects that improve model performance, efficiency, latency or reliability.
• Write production-grade code, i.e. tested, reviewed and scalable Python.
• Communicate findings clearly to get buy-in for recommended next steps.
• Work with your mentor toalign stakeholders, and drive next steps.

What This Job Offers

• Career Level: Intern
• Number of Employees: 1,001-5,000 employees