Machine Learning Engineer at Wikimedia Foundation

Summary

The Wikimedia Foundation is looking for a Machine Learning Engineer to join a small team spread across UTC -7 to UTC +3 and will report to the Director of Machine Learning, Chris Albon.  As a 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. One day you might work on deploying a model predicting whether an edit is vandalism, the next day you might be helping volunteers contribute to our machine learning models.

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, scabled 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:

• 2+ years of experience in an MLE/MLOps role as part of a team deploying production models.
• Experience planning, developing, training, documenting, deploying, and managing production machine learning models
• Experience with popular Python ML libraries like Scikit-Learn, XGBoost, etc.

• 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

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

U.S. Benefits & Perks*

• Health Care Benefits Covered at 100%: We cover 100% of premiums for medical, dental and vision plans for all full time employees and eligible dependents
• Wellness Reimbursement Program: Up to 1,800.00 USD per year for reimbursement for staff wellness expenses, such as gym fees, educational expenses, and more
• Technology and Equipment Stipend: In addition to receiving a brand new laptop, monitor, & docking station, all new hires receive 600.00 USD stipend to set up their space for working virtually 
• Professional Development Program: Up to 750.00 USD reimbursement per year to encourage continuous learning through attending conferences, courses, workshops and the purchase of educational materials 
• 401(k) Retirement Plan: Employer match of up to 4% of employee contributions dollar for dollar with no vesting period
• Paid Time Off: Generous paid time off policy of over 45 days, which includes: vacation days, at least one observed holiday a month, sick leave, and volunteer days
• Flexible Schedules: Options available to balance your personal and remote-work life
• Silent Fridays: No scheduled meetings so you can get caught up at the end of each week
• New Parent Leave: Fully paid new parent leave for seven weeks plus an additional five weeks for pregnancy, and flexible options as you embark on your return to work
• Fertility and Adoption Reimbursement Plan: Reimburses staff up to 5,000.00 USD in expenses per year, with a lifetime maximum of 10,000.00 USD
• Assistance for those unexpected life events: Long and short term disability, life insurance, and an employee assistance program
• Pre-tax Savings Plans: Generously funded health savings accounts (HSAs), pre-tax contribution options for health care, child care & elder care, public transportation and parking expenses

*Please note that for remote roles located outside of the U.S., we defer to our PEO to ensure alignment with local labor laws.