Software Engineer Intern – Systems & Data

Job Description

Software Engineer Intern — Systems & Data (Summer 2026) | Reml AI

The Tone:
This is a full-time internship at Reml AI, located remotely within the United States. Reml AI is a small team building an AI platform designed for institutional real estate investors, asset managers, and operators, helping them transition to continuous, bottom-up intelligence. This role is critical for building the core systems and data infrastructure behind Reml Insights, the company’s intelligence and decision-support engine for real estate investment.

The TL;DR
• Role: Internship
• Type: Full-time internship
• Location: Remote, United States

• Team: Works directly with the founder and CTO.
• Mission: Build the core systems for Reml Insights, transforming unstructured inputs into structured, reliable, and auditable data systems.
• Tech Stack: AWS, CLI tools, AI-assisted development workflows

What You’ll Actually Do
• Software Development: Ship production-quality software across backend, data pipelines, and frontend systems.
• Product Module Development: Develop core product modules, including the market rent engine and evaluation workflows.
• AI Systems Design: Design and build LLM agents, workflow agents, and automation systems.
• Technical Planning: Translate product and research requirements into clear engineering implementation plans.
• Data System Structuring: Transform unstructured inputs into structured, reliable, and auditable systems.

The Must-Haves
• Background: Student pursuing a degree in computer science, machine learning, engineering, or a related technical field, or equivalent practical experience.
• Experience: Strong hands-on coding ability, experience building production-quality software, and experience with AWS or other cloud infrastructure.
• Skills: Hands-on coding, production-quality software development, cloud infrastructure (AWS), AI-assisted development, LLM agent and workflow automation system building.
• Bonus: Interest in finance, real estate, or investment workflows; strong quantitative foundation; experience with data pipelines, backend systems, or full-stack product development; commitment to data quality and auditability.