R&D Management Trainee - Full-Stack Engineer (Backend）

About CWILL CWILL (pronounced 'quill') is a post-purchase and retention suite built for Shopify brands. Reduce support tickets, recover lost revenue from returns, and turn one-time buyers into loyal fans — with tools purpose-built for every touchpoint that follows the sale.

CWILL's engineering teams are building the next generation of SaaS products for Shopify brands — including AI-powered customer service tools and SEO optimization products. As our products grow, so does our need for strong engineering talent who can learn fast and ship real things. Our R&D Management Trainee program is built for engineers who are new to the workforce but ready to dig in. You'll be embedded in a product engineering team, working on production systems from day one with mentorship from senior engineers. Over time, you'll grow into an engineer who can independently own modules and make technical decisions. You'll be placed on one of our active product teams: • Order Tracking • Loyalty & Referrals • Pop-up & Email Marketing • AI Chat • SEO & Speed

Product Feature Development • Build features across the full stack with a backend focus: APIs, business logic, data processing, and third-party integrations • Participate in technical solution design, coding, integration testing, and deployment • Take ownership of the quality of the features you build and deliver them to spec System Maintenance & Bug Fixes • Assist with production bug investigation, root cause analysis, and resolution • Use logging, monitoring, and database tools to trace issues methodically • Contribute to ongoing improvements in system stability, performance, and code maintainability Engineering Collaboration & Growth • Follow team engineering standards: code reviews, documentation, and testing • Proactively communicate progress, risks, and blockers • Participate in post-launch retrospectives; continuously improve your code quality and engineering judgment • As you grow, take on increasing ownership of modules and technical decisions