The Mom Project is excited to support Etsy with their search for a Senior Machine Learning Software Engineer to join their team on a contract basis. About The Team Etsy is seeking a Senior Engineer for our Ads Quality team, one of the machine learning engineering teams for Etsy Ads, our on-site ad platform. Etsy Ads is used by our smallest and largest sellers to grow their businesses, and it is one of the major revenue streams for Etsy. The Ads Quality team focuses on determining which listings a buyer should be shown using machine learning techniques in order to maximize relevance, seller return, and Etsy revenue. This includes building machine learning systems designed to retrieve the most relevant ads for our buyers across a range of advertising surfaces throughout the buyer's journey. About The Role In this role, you will have the opportunity to work with our machine learning systems that impact our ad delivery system, including candidate retrieval, user engagement modeling, and ad experience on Etsy.com and the Etsy app. In addition to expanding our machine learning systems, you will work closely with other machine learning engineers and our product analytics partners to help drive product and customer impact.

Requirements

Must-Haves

• Solid engineering & coding skills and proficiency in at least one programming language of Python, Scala, Java, or C/C++.
• Experience with writing scalable and performant data pipelines on distributed systems
• Experience with the ML lifecycle: training, deployment, monitoring, debugging, and iterating on production machine learning systems

Nice-to-Haves

• Familiarity with vector databases, retrieval systems, and ANN tuning in production environments, with Vespa experience preferred.
• Hands-on experience in machine learning frameworks & technologies such as Tensorflow, Pytorch.

Hours and Location:

• M-F, 40 hours/week
• This role is 100% remote with standard EST hours

We offer a competitive salary range for this position. Most candidates who join our team are hired at the median of this range, ensuring fair and equitable compensation based on experience and qualifications.