• Build server-side game logic components and game simulations for real-money online gambling-style games (slot-focused). • Partner with mathematicians to evaluate and implement game feature feasibility and align behavior with specifications. • Collaborate with front-end game client developers to define response structures that maximize player experience. • Perform hands-on software design and development with a focus on maintainable, efficient, and well-structured code. • Solve technical challenges within internal game development frameworks and tooling. • Write unit tests and participate in peer design/code reviews; promote a strong culture of code quality. • Collaborate effectively with a remote, distributed team to promote high quality and timely completion of projects.

• Your main goal will be to build and evolve core platform foundations that power n8n’s workflow engine and frontend platform, enabling product engineers (and the open-source community) to ship confidently across cloud and self-hosted deployments. • As part of the team, you'll contribute to core platform domains and help improve reliability, scalability, and performance: • Backend platform foundations (execution, orchestration, data) • Contribute to backend subsystems like durable workflow execution & state, orchestration patterns, queue/task distribution, and system modularization. • Apply best practices for correctness and resilience (e.g., failure modes, idempotency, state handling) in systems that operate across cloud and self-hosted environments. • Improve data architecture and database performance, reliability, and maintainability. • Frontend platform foundations (canvas, collaboration, performance) • Work on the core canvas architecture and frontend platform primitives that enable complex workflow building experiences. • Build and improve real-time collaboration capabilities (e.g., WebSockets, CRDT-style approaches) with a focus on correctness and user experience. • Contribute to performance and rendering improvements (e.g., web workers, rendering optimization), and help strengthen platform contracts (plugin system, FE DX). • Technical contribution & collaboration • Contribute to technical decisions through clear proposals (e.g., RFCs) and discussions. • Collaborate with other teams to ensure platform changes integrate smoothly. • Support peers through code reviews, pairing, and knowledge sharing.

• Design and develop responsive, high-performance front-end applications using modern JavaScript/TypeScript frameworks (React, Next.js). • Build scalable backend services and RESTful or GraphQL APIs using Python (FastAPI, Django) or Node.js (Express, NestJS). • Integrate application components with Generative AI systems, including LLMs, streaming outputs (SSE, WebSockets), chat interfaces and document processing workflows. • Develop and maintain SQL and NoSQL database schemas and models, ensuring efficient data persistence and management. • Implement secure authentication and authorization mechanisms (OAuth 2.0, JWT, SSO). • Build and support CI/CD pipelines and containerized deployments using Docker, Kubernetes and GitHub Actions. • Collaborate with AI and Data Engineering teams to efficiently consume AI services, APIs and data pipelines. • Participate in code reviews, technical discussions and agile ceremonies, writing clean, testable and well-documented code. • Optimize performance, scalability and user experience across devices and platforms.

• Own backend features end-to-end: discovery, design, implementation, rollout, and long-term reliability/operations • Design and evolve distributed systems (services, pipelines, data stores) with strong performance, scalability, and resiliency • Build and maintain APIs (REST/GraphQL, where relevant) and data access patterns supporting analytics and search use cases • Establish strong engineering fundamentals: code reviews, testing strategies, incident readiness, and operational excellence • Apply an experimentation-first approach: define hypotheses, define success metrics/guardrails, run controlled rollouts and A/B tests when appropriate, and write clear readouts for stakeholders. • Bring evaluation discipline to AI-assisted work: treat prompts/configs like versioned artifacts, design regression tests, measure quality changes, and monitor for drift (the same way you would for performance or correctness) • Use GenAI productively and responsibly (for coding assistance, debugging, and design reviews), while maintaining high standards for correctness, security, and privacy. • Mentor teammates and raise the bar through documentation, pairing, and lightweight coaching on best practices