Manager, Software Engineering (Identity Engineering)
AffirmFull Time
Expert & Leadership (9+ years)
San Francisco, California, United States
Common questions about this position
The internships are in-person and located primarily in San Francisco, CA, with a small number available in New York City, NY. Applicants must be able to work in-person in one of these offices for the duration.
Applicants must be currently enrolled in a U.S. university undergraduate or graduate program with a graduation date of December 2026 or later, able to intern from June through August/early September 2026, and legally authorized to work in the US without visa sponsorship.
Relocation assistance is provided in the form of a relocation bonus for students who need to relocate to San Francisco or New York for the summer internship.
Interns work directly with engineers, product leaders, and designers, partnering with an engineering mentor and collaborating across teams including Product Designers, Product Managers, Sales, and users. The environment emphasizes curiosity, collaboration, and taking projects from ideation to deployment with feedback and customer metrics.
Strong candidates are current U.S. students graduating December 2026 or later, authorized to work in the US, available for the full summer in-person in SF or NYC, and demonstrate curiosity, collaboration, and interest in scalable systems and data analytics.
Cloud-based data analytics platform for businesses
Sigma Computing offers a cloud-based data analytics platform that enables businesses to analyze large volumes of data through a user-friendly, spreadsheet-like interface. Users can connect to their cloud data warehouse and access advanced features such as data collection, territory management, and revenue planning without needing coding skills. The platform is scalable, allowing for the analysis of billions of rows of data, and promotes self-service capabilities for faster insights. Recently, Sigma introduced AI features like data classification and natural language processing to enhance data analysis and support Enterprise AI initiatives.