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Ads Retrieval team’s mission is to identify the business opportunities, provide ML models and data driven solutions on candidate sourcing, recommendation, early ranking and filtering in Ads upper funnel. The team works on:

• Build and iterate on candidate sourcing and early ranking Machine Learning models and algorithms to find the most relevant, engaging and diversified ads candidates for global optimization and various product use cases.
• Design and establish a large scale candidate indexing system to enable efficient candidate retrieval at a scale of millions to billions, which powers ads recommendation and ranking with good balance between quality and computation efficiency.

As a senior machine learning engineer in the ads retrieval team, you will research, formulate and execute on our mission to build end-to-end ML solutions and deliver the right ad to the right user under the right context with data and ML driven solutions.

Your Responsibilities:

• Building ads retrieval and early ranking system for critical ML tasks with advanced industrial level techniques
• Research, implement, test, and launch new model architectures including information retrieval, ANN, recommendation system, deep neural networks within high dimensional information system
• Drive technical roadmaps and lead day to day project execution, and contribute meaningfully to team vision and strategy
• Work on large scale data systems, backend services and product integration
• Collaborate closely with multiple stakeholders cross product, engineering, research and marketing
• Be a mentor and cross-functional advocate for the team

Who You Might Be:

• 2+ years of experience with leading applied machine learning models with Tensorflow/Pytorch with large-scale ML systems
• 5+ years of end-to-end experience of training, evaluating, testing, and deploying machine learning models
• Proficiency with programming languages (Java, Python, Golang, C++, or similar) and statistical analysis.
• Experience of orchestrating complicated data pipelines and system engineering on large-scale dataset
• Prior experience with information retrieval and recommendation system
• Ads domain knowledge on product and ML solutions is a plus

Benefits:

• 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