Machine Learning Engineer Intern

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.

How you’ll make an impact:

• 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.

Minimum qualifications 

• 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.

Preferred qualifications

• 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. 

Position location This role is available in the following locations: Remote

Time zone requirements The team operates on the East/West coast time zones.

Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

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