Engineering Intern - Software - SWE/Machine Learning - ML

## About You As a Summer Engineering Intern at Cyvl, you won’t be stuck on side projects. You’ll work directly on the systems and features that power our platform — user interfaces, backend services and APIs, and the machine learning models. The work you do will make it into production, meaning you’ll see your impact live in front of customers. You’ll collaborate with our engineering and product teams to design and launch new capabilities, gaining exposure to a wide range of technologies and learning how to build scalable, production-grade software in a fast-moving startup environment. ## Key Responsibilities - Contribute to real product features that ship to customers, both frontend and backend. - Collaborate with engineers, designers, and product managers to build features that help governments build infrastructure faster. - Write well tested code for APIs and services using Python and Supabase. - Gain experience with cloud-based tools and DevOps workflows. - Develop real fluency with AI coding agents on a large production codebase: how to direct them, review their output, and know when to work by hand. - Work closely with engineers who will invest in your growth — regular pairing, thorough code reviews, and direct feedback on your work. ## Qualifications ## Required: - Currently pursuing a degree in Computer Science, Software Engineering, or a related field. - Strong programming skills in Python. - Basic understanding of databases and SQL (Postgres experience a plus). - Comfortable working with AI coding agents (Claude Code, Cursor, etc.) as part of a normal development workflow. - Excitement to learn new technologies and apply them quickly. - Strong communication skills and the ability to work in a collaborative environment. - Able to work in person at our Somerville, MA office. ## Nice to Have: - Experience with side projects, class projects, or hackathons that showcase full-stack development. - Familiarity with tools/frameworks like React, Supabase, Docker/Kubernetes, or cloud platforms (AWS, GCP, etc.). - Hands-on experience with computer vision — image classification, object detection, or segmentation (PyTorch, YOLO, Roboflow, or similar). - Experience working with messy real-world data: cleaning, labeling, or evaluating model output — not just training on a clean benchmark. - Interest in geospatial data, 3D mapping technologies, or infrastructure.