Full Stack Developer
Summary
The Full Stack Developer will own end-to-end feature development for large-scale cloud systems using Node.js, TypeScript, and React. The role emphasizes the integration of AI-assisted development tools into the engineering workflow for coding, debugging, and research.
- Own end-to-end feature development, including research, design, implementation, testing, deployment, and monitoring.
- Contribute to technological and architectural decisions.
- Develop testable, reusable, and efficient code to create high-performance applications.
- Analyze and improve the efficiency, scalability, and stability of various components across our system.
- Leverage AI-assisted development tools as part of the engineering workflow for coding, debugging, testing, research, and problem-solving.
- Explore and adopt new AI capabilities and development practices that can improve engineering velocity and software quality.
- Collaborate with Designers, Product Managers, Architects, and Software Engineers to deliver the best possible product to our customers.
- At least 4 years of experience as a Full Stack Engineer in a product company.
- Hands-on experience with Node.js, TypeScript, and React.
- Experience building and maintaining large-scale cloud systems.
- Hands-on experience with NoSQL databases.
- Practical experience using AI-powered development tools as part of day-to-day software engineering workflows.
- Ability to effectively use AI for tasks such as code generation and review, debugging, technical research, testing, and solution design.
- Strong analytical and problem-solving skills.
- Experience working in a Scrum team according to Agile principles.
- A team player with strong communication skills, a positive attitude, and a can-do approach.
- BSc in Computer Science, Software Engineering, or equivalent.
- Fluent English, written and spoken.
Advantages
- Hands-on experience with AWS.
- Experience working extensively with Claude / Claude Code or similar advanced AI coding assistants.
- Experience integrating LLMs or AI capabilities into engineering workflows, internal tools, or production systems.
- Familiarity with effective prompting, context management, and AI-assisted software development best practices.