Requirements:

Building industrial-level models for critical ML tasks with advanced modeling architectures and techniques
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 into features with various FE technologies such as aggregation, embedding, sub-models, etc.
Contribute meaningfully to team strategy. We give everyone a seat at the table and encourage active participation in planning for the future
Be a mentor and cross-functional advocate for the team

Nice to Haves:

Tracking records of consistently driving KPI wins through systematic works around model architecture and feature engineering
3+ years of experience with industry-level deep learning models
3+ years of experience with mainstream ML frameworks (such as Tensorflow and Pytorch)
4+ years of end-to-end experience of training, evaluating, testing, and deploying industry-level models
4+ years of experience of orchestrating complicated data generation pipelines on large-scale dataset
Experience with Ads domain is a plus
Experience with recommendation system is a plus

As a machine learning engineer in the Ads Conversion Modeling Team, you will take a leadership role in researching, formulating, and executing projects, while actively participating in the end-to-end implementation process.
You will collaborate with cross-functional teams to ensure successful product delivery.
You will also be able to contribute your expertise and shape the future of ads ML at Reddit!