Staff Machine Learning Engineer - Ads Conversion Modeling
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.
- 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.
- Comprehensive Healthcare Benefits
- 401k Matching
- Workspace benefits for your home office
- Personal & Professional development funds
- Family Planning Support
- Flexible Vacation (please use them!) & Reddit Global Wellness Days
- 4+ months paid Parental Leave
- Paid Volunteer time off
This job posting may span more than one career level.
In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.
To provide greater transparency to candidates, we share base pay ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.