In short

As a Senior Machine Learning Engineer at On, you'll play a critical role in the full lifecycle of our machine learning models. Besides being responsible for training, and deploying models, you will spearhead our MLOps initiatives to ensure their seamless and efficient integration and operation in production. This includes championing MLOps best practices, enhancing deployment processes, developing essential tooling and automation to maximize the impact of our AI solutions and implementing robust monitoring to optimize performance and reliability.

Your Mission

  • Lead the implementation and continuous improvement of our MLOps strategy, establishing best practices for model development, deployment, and monitoring.
  • Creating and training machine learning models to solve specific business problems, such as product recommendations, customer segmentation, and demand forecasting. Implementing such models into production systems to make predictions, drive real-time personalisation and support decision-making.
  • Design and build the necessary infrastructure and tooling to support efficient and scalable model deployment, including CI/CD pipelines and automated testing.
  • Implement and own Terraform to manage and provision our cloud infrastructure for machine learning operations.
  • Oversee the transition to a real-time streaming architecture for our machine learning applications, ensuring efficient data ingestion, feature engineering, and model serving in a streaming context.
  • Develop and implement a comprehensive monitoring framework to track model performance, identify potential issues, and ensure optimal model health in production. Monitoring model performance and updating them as needed to adapt to new data and changing conditions.
  • Collaborate closely with data scientists and engineers to ensure seamless integration of models into our existing systems and workflows. Stay abreast of the latest MLOps trends and technologies to continuously improve our processes and tools.
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  • Open planned office with men and women sat on a a large curved sofa
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  • Two women smiling at each other in a meeting room with large curved windows
  • A group of people sat around a table looking at large design papers
  • 3 men sat around with laptops smiling at each other
  • a large group of runners holding an On flag smiling at the camera
  • Man and woman sat at desks talking
  • Open planned office with men and women sat on a a large curved sofa
  • Group of runners talking outside the office
  • A team of people sat on the steps with a view of mountains outside the office.
  • Two women smiling at each other in a meeting room with large curved windows
  • A group of people sat around a table looking at large design papers
  • 3 men sat around with laptops smiling at each other
  • a large group of runners holding an On flag smiling at the camera
  • Man and woman sat at desks talking
  • Open planned office with men and women sat on a a large curved sofa
  • Group of runners talking outside the office
  • A team of people sat on the steps with a view of mountains outside the office.
  • Two women smiling at each other in a meeting room with large curved windows
  • A group of people sat around a table looking at large design papers
  • 3 men sat around with laptops smiling at each other
  • a large group of runners holding an On flag smiling at the camera

Your story

  • You have 5+ years of experience as a Machine Learning Engineer with a strong focus on MLOps. You have a proven track record of successfully deploying and managing machine learning models in production environments.
  • You possess deep knowledge of MLOps principles, tools, and best practices.
  • You are proficient in cloud platforms (Google Cloud Platform is preferred), infrastructure-as-code tools like Terraform
  • You have experience with CI/CD pipelines, containerization technologies (e.g., Docker), and orchestration tools (e.g., Kubernetes) and using orchestration tools such as Kubeflow (our preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows.
  • You have experience with real-time data streaming technologies such as Kafka and Confluent and feature stores in such settings.
  • You are skilled in building and maintaining monitoring systems for machine learning models.
  • You have excellent communication and collaboration skills, enabling you to effectively work with cross-functional teams.

Bonus:

  • Knowledge of frameworks such as LangChain used to orchestrate LLMs
  • Experience in LLM evaluations, debugging and monitoring using tools such as LangFuse or LangSmith
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About the Team

We're a growing team of passionate Data Scientists and Machine Learning Engineers working across On to build creative and impactful models end-to-end that personalise experiences, optimise decision making, and predict future trends. We sit within Technology and have the opportunity to collaborate across On - Optimising how we use data, how we consume data and how we support On's growth through data is something you could be a part of and we'd love to hear from you!

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A man in a blue shirt and a white t-shirt with a headset on is standing with his arms crossed and holding his chin looking into the distance

Explorer

Explorer

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Our team exists to think differently. Wild curiosity and a thirst for knowledge keeps moving our tech forward. There are no boxes here. We’re always dreaming of what’s possible – and searching for new ways to get there. 

Learn about out Spirits
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    Gioia

    Technical Product Team, Zürich

    My journey has evolved for sure and, actually, change can lead to infinite possibilities. Those core values that were there when I joined are still here today and I really find them extraordinary every day.
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    Vivek Juneja

    Engineering, Berlin

    We’re closely connected to the whole business. Every day, we’re making quick decisions to improve the customer experience. Because these live changes impact the whole business, we deeply respect individual opinion and what others think about the end customer experience.
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What we offer

On is a place that is centered around growth and progress. We offer an environment designed to give people the tools to develop holistically – to stay active, to learn, explore and innovate. Our distinctive approach combines a supportive, team-oriented atmosphere, with access to personal self-care for both physical and mental well-being, so each person is led by purpose.

On is an Equal Opportunity Employer. We are committed to creating a work environment that is fair and inclusive, where all decisions related to recruitment, advancement, and retention are free of discrimination.

Build the better you

What to expect

We want to set everyone up for success, so here’s the lowdown on how we hire. Our process is a two-way street – bringing you into our culture, while helping us learn how you think.

Our full process can last about eight weeks from application to offer, because we care about getting it right. These steps explain how we usually do things.

Before you get started, feel free to consider if you want to work with us. Strange question? Well, we give people a lot of space to navigate their day-to-day and that style isn't for everyone. We want you to be passionate about what you do and be sure this is the right fit. Because when skills and passion combine – it creates that 'Wow' moment.