Job Summary

The Wikimedia Foundation is looking for a Senior Machine Learning Operations Engineer to join a small team spread across UTC -5 to UTC +3 (East Coast US, Europe, and Africa) and will report to the Director of Machine Learning.  As a Senior Machine Learning Operations Engineer, you will be responsible for planning, developing, documenting, deploying, and managing production machine learning models. In this role, you will work with product teams, SREs, researchers, and the volunteer community on machine learning models making Wikipedia and similar projects better. One day you might work on taking a model in a Jupyter Notebook created by Wikimedia’s Research Team and re-implementing it as a scalable production machine learning model to predict whether an edit is vandalism, the next day you might help volunteers contribute to our machine learning models already in production.

You are responsible for:

• Working with internal customers (e.g. Wikimedia researcher who have created a proof-of-concept of a model) and external customers (e.g. Wikipedia editors and other volunteers) to deploy and manage productionized, scaled machine learning models.

Skills and Experience:

• 5+ years of experience in an MLE/MLOps role as part of a team deploying production models.
• Experience with end-to-end deployment of production machine learning models
• Strong English language skills and ability to work independently, as an effective part of a globally distributed team

Qualities that are important to us:

• Professionalism
• Positivity and solution focused
• Independently motivated
• Commitment to the mission of the organization and our values
• Commitment to our guiding principles
• Ability to disagree in a respectful manner and yet work towards a solution even when you disagree
• Good at async communication
• Solutions-focused.
• The Wikimedia ecosystem is complex, resources are limited, and our guiding principles are ambitious. We want you to work to find solutions embracing these factors.
• Self motivated with an ability to navigate through ambiguity and bring a project to completion with limited directions
• Curiosity and commitment to learn.

Additionally, we’d love it if you have:

• Experience with Docker, Kubeflow, or other MLOps systems
• Experience with volunteer communities and open source software development
• Experience with global coworkers
• Experience with remote work and/or async work

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