Senior Machine Learning Engineer
$15
US, Australia, UAE, Belgium, Colombia, Greece, Estonia, Brazil, Sweden, Croatia, Czech Republic, Italy, UK, Austria, Denmark, Canada, Finland, Poland, Switzerland, France, Netherlands, Spain, Germany, Ireland
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Senior Machine Learning Engineer

Overview

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 in 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.

Required skills and experience:

  • 5+ years of experience in an MLE and/or MLOps role as part of a team maintaining production models
  • Experience with popular Python ML libraries
  • Strong ML background, particularly solving NLP tasks
  • Strong English language skills
  • Experience with global coworkers
  • Experience with remote work and/or async work

Additionally, we’d love it if you have:

  • Experience in fine-tuning pre-trained models
  • Familiarity with ETL processes and data pipeline orchestration
  • Experience with any pipeline orchestration tool (Airflow, Kubeflow, Argo workflows etc)

Qualities that are important to us:

  • Great coworker on remote teams
  • Experience with volunteer communities and open-source software development
  • Positivity and solution focused
  • Independently motivated

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 recruiting@wikimedia.org or +1 (415) 839-6885.

Tagged as: 5+ Years, machine learning, Python

Senior Machine Learning Engineer
$15
US, Australia, UAE, Belgium, Colombia, Greece, Estonia, Brazil, Sweden, Croatia, Czech Republic, Italy, UK, Austria, Denmark, Canada, Finland, Poland, Switzerland, France, Netherlands, Spain, Germany, Ireland
auto-extracted
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