IT Developer
Ort: Taipei, Taiwan · Bereich: Engineering & Development · Level: Mid-Level · Veröffentlicht: 2. Oktober 2026 · Geprüft: 5. Okt. 2026
Rollenübersicht & Beschreibung
Über das Unternehmen
Appier
appier.comAI software for advertising, personalisation and customer data activation.
Ähnliche Positionen im DACH-Ökosystem
Verwandte Rollen und Profile aus dem AdTech-, Media- und Datenmarkt
This role provides 2nd level IT systems support for Energy Group, focusing on maintenance, troubleshooting, and resolving technical issues to ensure system reliability. The engineer handles day-to-day operational incidents and escalations.
Leads engineering teams across Germany and Switzerland, setting technical strategy and overseeing delivery of digital projects. Focuses on scaling engineering capabilities and driving technical excellence within the agency.
This software engineering internship in Zurich focuses on developing scalable backend systems. Interns will contribute to Pinterest's core technology stack.
This senior engineering role builds and scales Contentful's personalization capabilities, integrating with the core content platform to deliver tailored experiences. The engineer works on backend services, APIs, and data pipelines to support real-time personalization.
Weitere Positionen bei Appier
3 weitere offene Stellen
This role is responsible for ad sales, focusing on selling advertising inventory and solutions to clients. The Sales Manager will build and maintain client relationships, negotiate deals, and achieve sales targets.
This role focuses on building and maintaining frontend applications for Appier's AI-driven marketing platform. The engineer will work on user interfaces that support AI-powered advertising and marketing solutions, requiring expertise in modern JavaScript frameworks and performance optimization.
This Senior Software Engineer role focuses on backend development for the REC (Recommendation) system, which powers personalized ad recommendations. The engineer will design and build scalable backend services to serve recommendations in real-time. Key challenges include optimizing for low latency, handling high concurrency, and integrating with machine learning models.
