On - Machine Learning Engineering Manager - Zürich

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Technology

In short

Your mission as a ML Engineering Manager is to lead, and manage the end-to-end delivery of cutting-edge, production-grade Machine Learning and AI platforms. This role requires you to set the technical direction for your domain and champion MLOps best practices, ensuring a focus on scalable and reliable systems. While our foundation is built on traditional MLOps, we are rapidly expanding into Agentic Intelligence. You will play a pivotal role in evolving our infrastructure to support autonomous agents that can reason, use tools, and drive business impact.
You will not just lead a team; you will build the backbone of AI at On, turning massive data streams into the competitive edge that powers our global growth.

Your mission

Your primary objectives as an ML Engineering Manager are:

– Provide Technical Leadership & Architectural Vision: Be accountable for your team’s technical decisions and solutions, ensuring they align with MLOps best practices and On Tech’s architecture north star, security, and compliance guidelines.
– Drive Strategic Alignment & Business Impact: Partner with cross-functional stakeholders to understand business requirements, and proactively identify and deliver ML solutions that have a significant impact on stakeholders across functions or regions.
– Lead Decisive Execution: Enable the team to make timely and informed decisions for consequential, potentially irreversible decisions, and provide direction/support to others in tackling complex, new problems by identifying underlying issues and root causes.
– Ensure Operational Excellence: Own the delivery and reliable operation of production ML/AI platforms, ensuring timely delivery, managing risk, and maintaining systems in accordance with established SLOs (Service Level Objectives), appropriate metrics, monitoring, and security.
– Architect the Agentic Future: You will oversee the development of Agentic Platforms and help the team navigate the transition from static model serving to dynamic agent orchestration, including reasoning loops and tool-augmented generation.
– Champion Team & Talent Development: Actively promote formal and informal mentoring, provide growth opportunities to your team, and build an inclusive team environment that fosters a culture of seeking out and delivering candid feedback.

Your story

You are a proven domain expert and leader ready to manage one or more engineering teams, accountable for technical delivery, quality, and hiring in the ML platform space. You should be able to demonstrate:

– Deep Domain Expertise: 8+ years of related experience or equivalent, with deep technical expertise in ML and AI production implementation and MLOps and AgentOps principles, including a strong track record in building and operating robust, end-to-end machine learning pipelines.
– Proven People Leadership: Proven experience in managing one or more teams with Individual Contributors (ICs) under direct management. You possess the ability to empower your team to ship high-quality code at pace, helping them navigate trade-offs between perfect and ‘production-ready.
– Cloud & Platform Fluency: Expert knowledge of technology concepts such as streaming, architecture and AI-components like model stores or feature stores, with hands-on experience on cloud platforms (GCP preferred) and automated CI/CD for ML.
– Collaborative Influence: You are an exceptional communicator and a genuine team player, adept at guiding team decisions, fostering consensus through professional influence, and effectively conveying complex technical information to diverse audiences.

Meet the team

You will be part of a growing and diverse team of ML engineers, data scientists, data engineers, and product managers passionate about revolutionizing how we leverage AI/ML to solve complex challenges across On.
We focus on building and operating the creative and impactful models that personalize experiences, optimize decision-making, and predict future trends.
The team operates in a fast-paced environment and is used to rapid turnaround times and ambitious targets. The shared goal is efficient growth at high speed, ensuring our ML systems scale with On’s needs.