Software Engineer 1

About Condé Nast

Engineering Team

About the Role

Front-End Responsibilities

• Build polished React UI components with attention to layout, typography, and interactive fidelity.
• Use modern React and TypeScript features, including hooks, suspense, SSR, hydration, and RSC concepts.
• Write reusable semantic code designed for long-term scalability.
• Work with GraphQL schemas, fragments, resolvers, and component-oriented queries.
• Improve Core Web Vitals and client-side performance.
• Ensure accessibility, semantic HTML, responsive behavior, and robust interactions across devices and markets.

AI-Assisted Engineering

• Use AI-assisted engineering tools for discovery, prototyping, testing, documentation, and implementation while owning code quality.
• Critically evaluate AI-generated code through tests, peer review, observability, and engineering judgment.
• Identify responsible AI opportunities for publishing, developer experience, content operations, and front-end platform efficiency.
• Address confidential data, copyright, bias, attribution, human review, and editorial integrity in AI practices.

Platform Contributions

• Extend and refine shared component libraries to support distinctive brand layouts without sacrificing consistency.
• Contribute to configuration-driven systems that allow brand expression while remaining maintainable at scale.
• Participate in design reviews, architectural discussions, and platform strategy.
• Follow engineering standards for code quality, accessibility, and performance.

Required Experience and Skills

• 2+ years of front-end development experience using React and TypeScript.
• Strong GraphQL knowledge, including fragments, schemas, and component-oriented queries.
• Understanding of modern React, CSS, accessibility, Core Web Vitals, and runtime performance.
• Experience with AI-assisted development tools and the ability to test and refine AI-generated outputs.
• Awareness of responsible AI issues including privacy, security, intellectual property, accessibility, bias, and editorial trust.
• Curiosity about AI-enabled product experiences for publishing, storytelling, personalization, discovery, or internal tooling.

Nice to Have

• Experience integrating AI-powered features such as streaming responses, semantic search, recommendations, summarization, or editorial assistance.
• Familiarity with LLM APIs, embeddings, RAG patterns, evaluation workflows, or AI observability.
• Experience designing interfaces for AI-assisted workflows with feedback, confidence cues, human review, and graceful failure states.