Summary

The Wikimedia Foundation is looking for a Senior Machine Learning Engineer to join a small team spread across UTC -5 to UTC +3 (Eastern Americas, Europe, and Africa) and will report to the Machine Learning Engineering Manager, Ilias Sarantopoulos. As a Senior Machine Learning Engineer, you will be responsible for planning, developing, training, 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.

You are responsible for:

• Working with internal customers (e.g. other teams inside the Wikimedia Foundation) and external customers (e.g. Wikipedia editors and other volunteers) to build, deploy, and manage productionized, scaled machine learning models. This includes communicating with the customers early to assess needs, working with them to scope out the appropriate tooling that might be needed, gathering the training data, training the model is a repeatable process, deploying the model on a deployment cluster, and monitoring that model over time.
• Helping other teams at the Foundation and the broader community understand the work conducted on the team.

Skills and Experience:

• 5+ years of experience in an MLE and/or MLOps role as part of a team deploying production models.
• Experience with popular Python ML libraries.
• Strong English language skills.
• Experience with global coworkers.
• Experience with remote work and/or async work.

Qualities that are important to us:

• Able to work collaboratively on remote teams.
• Experience with volunteer communities and open-source software development.
• Positivity and solution focused.
• Independently motivated.

Additionally, we’d love you to fit one of three skill sets:

• ML infrastructure: You have interest and experience in building backend production ML systems. You understand how real world MLOps systems work and how to use infrastructure to maximize the performance of models. 
• Model development and maintenance: You have interest and experience converting a model from a researcher into an efficient, high performing production ML service. You understand how to expand on the work of a researcher to develop and maintain models in production.
• ML product Integration: You have interest and experience in helping front-end and feature product teams effectively use machine learning models. You understand the needs and challenges of product teams using machine learning and have built services that help make ML-related work of those teams easier and faster.

About the Wikimedia Foundation

The Wikimedia Foundation is the nonprofit organization that operates Wikipedia and the other Wikimedia free knowledge projects. Our vision is a world in which every single human can freely share in the sum of all knowledge. We believe that everyone has the potential to contribute something to our shared knowledge, and that everyone should be able to access that knowledge freely. We host Wikipedia and the Wikimedia projects, build software experiences for reading, contributing, and sharing Wikimedia content, support the volunteer communities and partners who make Wikimedia possible, and advocate for policies that enable Wikimedia and free knowledge to thrive. 

The Wikimedia Foundation is a charitable, not-for-profit organization that relies on donations. We receive donations from millions of individuals around the world, with an average donation of about $15. We also receive donations through institutional grants and gifts. The Wikimedia Foundation is a United States 501(c)(3) tax-exempt organization with offices in San Francisco, California, USA.

As an equal opportunity employer, the Wikimedia Foundation values having a diverse workforce and continuously strives to maintain an inclusive and equitable workplace. We encourage people with a diverse range of backgrounds to apply. We do not discriminate against any person based upon their race, traits historically associated with race, religion, color, national origin, sex, pregnancy or related medical conditions, parental status, sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, or any other legally protected characteristics.

The Wikimedia Foundation is a remote-first organization with staff members including contractors based 40+ countries*. Salaries at the Wikimedia Foundation are set in a way that is competitive, equitable, and consistent with our values and culture. The anticipated annual pay range of this position for applicants based within the United States is US$109,047 to US $169,455 with multiple individualized factors, including cost of living in the location, being the determinants of the offered pay. For applicants located outside of the US, the pay range will be adjusted to the country of hire. We neither ask for nor take into consideration the salary history of applicants. The compensation for a successful applicant will be based on their skills, experience and location. 

*Please note that we are currently able to hire in the following countries: Australia, Austria, Bangladesh, Belgium, Brazil, Canada, Colombia, Costa Rica, Croatia, Czech Republic, Denmark, Egypt, Estonia, Finland, France, Germany, Ghana, Greece, India, Indonesia, Ireland, Israel, Italy, Kenya, Mexico, Netherlands, Nigeria, Peru, Poland, Singapore, South Africa, Spain, Sweden, Switzerland, Uganda, United Arab Emirates, United Kingdom, United States of America and Uruguay.  Our non-US employees are hired through a local third party Employer of Record (EOR). 

We periodically review this list to streamline to ensure alignment with our hiring requirements. 

All applicants can reach out to their recruiter to understand more about the specific pay range for their location during the interview process.

If you are a qualified applicant requiring assistance or an accommodation to complete any step of the application process due to a disability, you may contact us at [email protected] or +1 (415) 839-6885.

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