Member of Technical Staff, Intern

Pace
New York, New York, United StatesPosted Sep 14, 2026

About the role

Member of Technical Staff, Intern Pace is the AI operations platform for insurance, trusted by some of the leading carriers, specialty mutual groups, and tech-forward brokers. Almost $400bn per year is spent on outsourcing in financial services. We’re here to change that, working side-by-side with some of the largest companies in the world. Role We’re looking for a Member of Technical Staff, Intern to join our product and engineering team for the summer. This is not a typical internship. You’ll be treated as a full member of the team from day one, owning a meaningful problem and shipping work used by our customers and insurance operations teams. Depending on your interests and the highest-priority needs of the team, you might: Build full-stack product features for browser automation and AI-powered workflows Work with our largest customers to understand their operations and build features that help them scale Develop evaluations, observability, and internal tools that make our agents more reliable You’ll work alongside our engineers and users to take a problem from initial understanding through implementation and launch. Internship Details Duration: 10–14 weeks Location: In person in New York Dates: Flexible start dates (May or June) Compensation and Benefits: Competitive compensation and generous meal stipend for lunch and dinner Full-time opportunity: Strong performers may be considered for a full-time role after graduation Your background

You might be a strong fit if you

Have built something from scratch through a class, research project, internship, startup, open-source contribution, or personal project Have a strong software engineering foundation and experience programming in at least one language Enjoy working through ambiguous problems and learning by building Take ownership, move quickly, and seek feedback Care about the people using what you build, not just whether the code works Are excited about building dependable AI systems for complex, real-world e

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