Senior / Lead Applied AI Engineer- AI Labs at On in Zürich

Job description

In short At On, everything is deeply rooted in innovation and human movement. Within the AI Lab and Emerging Technology Incubation team, the focus centers on exploring, prototyping, and building frontier digital products and agentic systems that help everyday people build a joyful, lifelong relationship with movement while transforming organizational operations. This Lead Software Engineer role serves as a core technical anchor across backend, data, and applied-AI architectures, blending early-stage product exploration with the engineering rigor needed to deploy scalable, production-ready systems. This position reports to Senior Lead - Staff Software Engineer for AI Labs. Your Mission Navigate from ambiguity to production: Lead the backend, data, and applied-AI architecture across emerging incubation ventures while contributing across frontend and mobile surfaces to ship cohesive vertical slices. Partner closely with Product, Design, and Research to translate early-stage hypotheses into structured domain models, clear technical RFCs, and production-ready applications. Architect Resilient AI & Real-Time Systems: Design and construct the core infrastructure for interactive, multimodal agentic experiences, spanning low-latency streaming runtimes, tiered agent memory structures, hybrid retrieval pipelines, and programmatic evaluation frameworks. Bridge Incubator Agility with Enterprise Scale: Validate user value at early-stage speed while establishing cloud-native (GCP, GKE), secure, observable foundations that transition smoothly into platforms across wider engineering clusters. Champion Principled AI-Native Engineering: Leverage frontier AI tooling and coding agents to explore unfamiliar domains rapidly, pairing velocity with strict type safety, deep system comprehension, and maintainable code that elevates engineering standards across the squad. Cultivate Cross-Functional Collaboration: Serve as a collaborative technical partner across teams in Zurich, prototyping partners in London, and cross-functional stakeholders, shaping both strategic product direction and technical execution. Your Story Note: Even without checking every single box below, individuals with relevant experience, curiosity, and a collaborative mindset are encouraged to apply. Proven experience building production-grade multimodal agentic systems, including components such as tiered agent memory, multimodal ingestion, RAG, real-time voice or streaming pipelines, tool integration protocols (such as MCP), and automated evaluation workflows. A degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent practical experience. Demonstrated track record operating in high-ambiguity environments with a strong product mindset, paired with experience navigating enterprise cloud infrastructure (GCP, GKE) and aligning stakeholders through architectural RFCs. Strong engineering foundations in Python (FastAPI, Pydantic, asyncio) and/or TypeScript (NestJS), with depth in relational and vector data modeling (PostgreSQL, pgvector), event-driven architectures, and API design. Practical background in building and scaling distributed backend systems, alongside the adaptability to support frontend/mobile applications (React, TypeScript, Flutter) or container infrastructure (Docker, Kubernetes) when initiatives require it. A collaborative mindset skilled at partnering with Product, Design, and Research to shape early concepts into reliable software solutions. A self-directed learner driven to explore emerging technical domains and tackle complex, evolving problems. Valued additions: Familiarity with real-time multimodal streaming runtimes (such as LiveKit, WebRTC, Gemini Multimodal Live, OpenAI Realtime, or ElevenLabs) and low-latency interaction patterns (barge-in handling, prefetching, asynchronous tool dispatching). Experience collaborating with external technology partners or working within health, human movement, or advanced R&D incubation