Member of Technical Staff (AI Products, Early Career, Industry)

Join our team as a Member of Technical Staff in AI Products. This full-stack role offers the opportunity to own a user-facing surface end to end, from design to application logic and data models. You will ship to production continuously, build with AI as a primary tool, and work directly with designers, PMs, and research engineers. Ideal candidates have 1+ years of professional software engineering experience, strong backend language skills, and hands-on experience with AI products.

• Posséder une surface utilisateur de bout en bout, de l'interaction, du design et du frontend à la logique d'application, aux API, aux modèles de données.
• Transformer des idées de produits ambiguës en prototypes fonctionnels, puis utiliser des données d'utilisation réelles pour décider de ce qui survit.
• Construire avec l'IA comme outil principal, en utilisant des agents et des modèles de codage de manière agressive pour avancer plus rapidement.

• This role is for industry candidates already working in software engineering for the past 1+ years
• Comfort with ambiguity and an instinct to start building incrementally rather than wait for a spec
• Strong in at least one backend language, such as Python or Go, with real experience across services, APIs, and relational data modeling. Comfortable working in React and TypeScript on the frontend
• Real evidence of building: production work you understand in depth, shipped side projects with actual users, meaningful open-source contributions, hackathon wins, or research you turned into working software
• Hands-on experience with AI products, with opinions about their rough edges and what that means for the product you are building
• 1+ years of professional software engineering experience, or a track record of strong internships where you shipped production code at scale
• Experience with our stack: React or Next.js, Python, Go, PostgreSQL, Redis, Docker, AWS. You will not be expected to know all of it on day one
• Product taste. You can tell a good interface from a bad one and explain how to improve it
• Experience building on top of LLMs: AI orchestration, RAG, evals, agents, tool use
• A public track record we can read, such as GitHub, a technical blog, or a demo we can try