Software Engineer Intern - PFE 2027

About us

At Cognira, we strongly believe that people are the biggest asset of our company. Our hand-picked team consists of passionate, collaborative, and forward-thinking individuals from all over the globe. We are passionate about making science easy and accessible to retailers, helping them get more value from people, data, and systems.

We bring together expertise in retail, science, and scalable technologies to automate and enhance the quality of decision-making through software and consulting services. We are proud to have a growing team of domain experts and data scientists, as well as a culture that fosters strong and long-lasting relationships with our clients.

Our Locations: Atlanta, GA, US Tunis, Tunisia Istanbul, Turkey Paris, France Porto, Portugal Our values: Be nice and trustworthy Put our customers first Succeed together Never stop improving Are you ready to grow with us? To find out more about Cognira, please visit our website at www.cognira.com Please submit your resume in English.

I- Frontend Team Internship Topics: Topic1 : A Visual Screen Builder with AI-Ready Design Handoff Project Overview: Cognira's design system is documented in Storybook, but Product Managers and UX designers currently require engineering support to assemble screens using available components. Additionally, design handoff between design and engineering teams remains a manual and error-prone process.

This project will create a visual screen builder that allows non-technical users to construct screens from existing Storybook components, generate structured specifications, and expose those specifications to AI coding agents through an MCP server. Deliverables Screen Builder Application Component catalog automatically generated from Storybook metadata.

Visual canvas for building screens using predefined layouts. Auto-generated property editor based on TypeScript component definitions. Save and load functionality for screen configurations. Screen Specification Framework JSON Schema automatically generated from component prop definitions. Validation of screen specifications against generated schemas.

Human-readable and AI-readable Markdown exports. MCP Integration An MCP server that exposes screen specifications to AI coding agents. Support for AI-powered validation and implementation using approved components and properties.

Required Skills

TypeScript and React Front-end application development JSON and schema design Understanding of component-based design systems Problem-solving and software architecture skills Preferred Skills Storybook MCP (Model Context Protocol) TypeScript metadata and code generation Design systems and UX workflows AI-assisted development tools API and server development Topic2: Interactive MCP Apps for PromoAI's Agent Cora Project Overview PromoAI is a SaaS platform that helps retailers plan, forecast, and analyze promotional performance.

Its AI assistant, Cora, answers business users' questions in natural language, helping them understand forecasts, identify promotion conflicts, and gain insights into their promotional activities. Cora can already be accessed through AI applications such as Claude using the Model Context Protocol (MCP), but responses are currently delivered as plain text.

MCP Apps, a recent extension to the protocol, allow tools to return interactive web applications directly within AI chat experiences. In this project, you will enhance Cora's user experience by building interactive MCP Apps that allow users to explore charts, sort and export tables, select products and stores, and interact with forecast data directly from their AI assistant.

You'll work across both a TypeScript backend and a modern web frontend while validating the experience across multiple AI platforms. Deliverables Implement MCP Apps support within Cora's MCP server, providing interactive experiences alongside text-based responses for clients that do not support MCP Apps. Develop a library of reusable interactive views, including: Answer cards with charts and data tables Promotion and event information cards Forecast breakdown visualizations Promotions calendar views Build interactive components that communicate directly with backend services, including: Product and store pickers Alerts inbox Exportable data tables Implement adaptive styling that follows the theme of each host AI application in both light and dark modes.

Create automated tests covering both frontend components and backend server changes. Produce a compatibility report for Claude, ChatGPT, Gemini, and VS Code. Stretch Goal: Develop a shared component library that can be used across both Cora's internal chat interface and external AI applications.

Required Skills

Web Development TypeScript / JavaScript React Vite Nginx Fundamental Knowledge LLM tool calling AI agents Model Context Protocol (MCP) Automated testing Preferred Skills Python development Web security fundamentals, including iframes, Content Security Policy (CSP), and OAuth Building interactive charts and data tables for web applications UI component design and frontend performance optimization II- Backend Team Internship Topics: Topic 1: Test-Impact Selection Service Project Overview Currently, every code change triggers the entire test suite, even when only a small portion of the codebase is affected.

This project aims to build an intelligent test-selection service that analyzes code changes, determines impacted areas, and executes only the relevant tests while providing clear explanations for each selection. Deliverables Build a service that identifies and executes tests affected by a code change. Provide explanations showing why each test was selected.

Generate replay reports using historical commits to demonstrate reduced testing time without missed failures. Integrate the solution into the CI/CD pipeline. Deliver technical documentation and a final project demonstration.

Required Skills

Scala Software testing fundamentals Object-oriented programming Git Preferred Skills PostgreSQL SemanticDB CI/CD pipelines Static code analysis Software architecture and dependency analysis Topic 2: Human-in-the-Loop AI Development Assistant Project Overview As AI-generated code becomes increasingly common, developers need tools that provide visibility, review capabilities, and human oversight throughout the development process.

This project focuses on building a full-stack application that enables developers to review AI-generated code changes, request explanations, collaborate on reviews, and maintain control over AI-assisted development workflows. Deliverables Build a web application for reviewing AI-generated code changes. Enable developers to comment on specific code blocks.

Allow users to request explanations and clarifications for generated code. Provide pull-request walkthrough capabilities. Create a collaborative review experience that combines AI assistance with human validation. Deliver documentation and a final demonstration.

Required Skills

Full-stack web development React SQL databases Software engineering fundamentals Git and collaborative development workflows Preferred Skills Go or Python PostgreSQL AI-assisted development tools Large Language Models (LLMs) Code review and developer productivity tools