React.js Node.js Fullstack Developer

Descrição da Vaga

Requisitos

• Own data modeling and database performance — schema design, query optimization, and long-term maintainability
• Partner closely with product and design to translate ambiguous requirements into shipped systems
• Set and uphold engineering quality standards through reviews, testing, and technical leadership
• Required Background
• Professional experience building and owning production fullstack systems
• Strong TypeScript experience across frontend and backend
• Frontend experience building real products with React
• Strong command of PostgreSQL and relational data modeling
• Experience designing and maintaining RESTful APIs used at scale
• Working knowledge of AWS or comparable cloud platforms
• Experience with microservices and/or serverless architectures
• Familiarity with Redis, queues, background workers, or async job systems
• Strong systems thinking, ownership mindset, and attention to detail
• Experience with CI/CD, infrastructure as code, or DevOps-leaning workflows
• Background building cloud-native, production-grade systems from scratch
• Exposure to AI-adjacent, data-heavy, or ML-powered products
• Prior experience in high-velocity startup or lab-style environments where scope is fluid and impact is high

Responsabilidades

• Location: Remote (Must overlap PST timezone at least 6-8 hours)
• You’ll own core product surfaces end-to-end — spanning architecture, backend systems, frontend execution, and production reliability
• You’re expected to make real architectural calls, raise the engineering bar, and move fast through ambiguity
• Design and own end-to-end fullstack systems powering core product workflows
• Architect and build clean, scalable backend services and APIs with strong contracts and clear ownership
• Develop high-quality, performant frontend interfaces that directly ship to users
• Debug and resolve production issues with urgency; improve reliability, observability, and failure modes
• Make pragmatic tradeoffs between speed and correctness in a fast-moving AI-lab environment