Staff Machine Learning Engineer (Ads Conversion Modeling)

RedditRemote; Ontario, CanadaFull-timeJan 12, 2024
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Reddit is a community of communities where people can dive into anything through experiences built around their interests, hobbies, and passions. Our mission is to bring community, belonging, and empowerment to everyone in the world. Reddit users submit, vote, and comment on content, stories, and discussions about the topics they care about the most. From pets to parenting, with over 100,000 active communities and over 70 million daily active users, it is home to the most open and authentic conversations on the internet. For more information, visit redditinc.com.

We’re evolving and continuing our mission to bring community, belonging, and empowerment to everyone in the world. Providing a delightful and relevant experience to our users applies to our Ads like all of our offerings, and we’re excited to build a product that is best-in-class for our users and advertisers. The year ahead is a busy one - join us! 

The Ads Conversions Modeling Team is entrusted with the development and maintenance of a diverse set of Machine Learning models that are responsible for predictions regarding user conversions after engaging with Reddit. The creation and enhancement of these models plays a crucial role in our organization's efforts to optimize advertising effectiveness and drive business growth.

We are seeking a highly skilled Machine Learning specialist to take a leadership role in the advancement of state-of-the-art conversion models. You will serve as a visionary in designing these models, actively participate in the end-to-end implementation process, and collaborate with cross-functional teams to ensure successful product delivery. You will also have the opportunity to contribute your expertise and shape the future of conversion modeling at Reddit.

Your Responsibilities:

  • Develop advanced and scalable deep learning models using cutting-edge techniques for critical machine learning tasks within the conversions modeling domain.
  • Design and implement innovative strategies for signal loss mitigation, ensuring the accuracy and reliability of predictions in the presence of incomplete or noisy data.
  • Research, implement, test, and launch new model architectures including deep neural networks with advanced pooling and feature interaction architectures.
  • Systematic feature engineering works to convert all kinds of raw data in Reddit (dense & sparse, behavior & content, etc) into features with various FE technologies such as aggregation, embedding, sub models, etc.  
  • Be a mentor and cross-functional advocate for the team.
  • Contribute meaningfully to team strategy. We give everyone a seat at the table and encourage active participation in planning for the future!

Who You Might Be:

  • 2+ years of experience of leading an ads modeling team with DNNs as the primary model.
  • 1+ years of experience in signal loss mitigation (through transfer learning, data augmentation, and/or ensemble methods).
  • 4+ years of experience with industry-level deep learning models.
  • 4+ years of experience with mainstream ML frameworks (such as Tensorflow and Pytorch).
  • 5+ years of end-to-end experience of training, evaluating, testing, and deploying industry-level models.
  • 5+ years of experience of orchestrating complicated data generation pipelines on large-scale datasets.
  • Track record of consistently driving KPI wins through systematic works around model architecture and feature engineering.

Benefits:

  • Comprehensive Health benefits
  • Retirement Savings plan with matching contributions
  • Workspace benefits for your home office
  • Personal & Professional development funds
  • Family Planning Support
  • Flexible Vacation & Reddit Global Days Off

Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please contact us at [email protected].

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