QA Engineer Intern

Who you are

• You are currently in your final year (BAC+5, Master 2 or engineering school) in Computer Science, Software Engineering, Data, or AI
• You have solid programming skills in Python, TypeScript/JavaScript, or C#
• You have a good grasp of software testing fundamentals, Git, and REST APIs
• You are autonomous and rigorous, with a strong sense of ownership
• Nice to have: hands-on experience with LLMs, prompt engineering, or agent frameworks (LangChain, Copilot, OpenAI/Claude APIs); test automation (Playwright, Cypress); CI/CD (GitHub Actions, TeamCity); Docker/Kubernetes; SQL or lightweight dashboards (Streamlit, React, FastAPI)
• You can provide a 6-month internship agreement (Convention de stage) signed by your school/university, starting around February 2027
• Language requirements: English is mandatory at a fluent level (French is a plus)

What the job involves

• The Quality Assurance team, part of Engineering, makes sure that Shift can ship fast and safely
• Our AI-driven software is deployed for insurers worldwide, across many multi-tenant environments, and we use Generative AI, LLM agents, and modern automation to solve quality challenges at this scale
• Release validation: risk-based testing and delivery automation
• Automated regression suites and test automation
• Functional test coverage and test gap analysis
• Test data generation and test environment provisioning
• AI-powered QA tooling, built with LLMs and agents, that engineers and QA leads use every day
• Working with Engineering teams and QA leads, we replace manual QA effort with intelligent tooling, and we measure the time it saves
• You’ll own an end-to-end project that moves a real QA bottleneck from manual effort to intelligent tooling
• You will build something the QA engineering teams use in their day-to-day work, not a proof of concept that stays on a shelf
• Your subject will be defined with you during the selection process, within one of these example missions:
• AI-driven release validation: build a “release copilot” that analyzes release tickets and code changes to compute risk scores and generate risk-based test plans. It also helps run bug bash sessions (scope, assignment, triage, duplicate detection) and drafts test reports and release notes
• Functional coverage and test gap analysis: use code intelligence and LLMs to detect shipped features, feature flags, and endpoints, map them against environment configurations and automated regression suites, and show untested areas in a live coverage dashboard
• Synthetic test data and environment provisioning: design a data generation and seeding pipeline (CLI / API) that produces realistic, deterministic, and privacy-compliant datasets, so teams can provision and reset test environments on demand, integrated into CI/CD
• Along the way, you’ll learn how QA, Engineering, and Product work together to ship AI software to insurers around the world. Your project will go beyond a prototype: you will validate it on real releases, hand it over with documentation, and present the results to leadership

Benefits

• Flexible remote and hybrid working options
• Competitive Salary and a variable component tied to personal and company performance
• Company equity
• Generous PTO and paid holidays
• Parental leave opportunities
• Extensive mental health benefits provided via our global Employee Assistance Program
• Paid volunteering time - 16 hours annually