Senior Machine Learning Engineer, Ads Targeting

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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!

Ads Targeting ML engineers are focused on designing and implementing ML systems and solutions for improving targeting products. The team’s projects involve building large-scale offline & online retrieval systems across several dimensions to improve contextual & behavioral targeting for targeting products.

As a senior machine learning engineer in the ads targeting core team, you will execute our mission to automate targeting and deliver the most relevant audiences to advertisers under the right context with data and ML-driven solutions.

Responsibilities:

• Own end-to-end execution of ML-based targeting products like smart targeting expansion, keyword targeting, auto targeting, user lookalikes etc
• Own offline & online experimentation of ML models for improving targeting products to drive advertiser outcomes
• Research, implement, test, and launch new model architectures for retrieval using deep learning (GNNs, transformers, two tower models) with a focus on improving advertiser outcomes
• 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

Required Qualifications:

• 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
• Experience with large scale data processing & pipeline orchestration tools like Spark, Dataflow, Kubeflow, Airflow, BigQuery
• Experience working with nearest-neighbor search systems is a big plus
• Experience building & improving MLOps tools and ML experimentation workflows
• Experience working with cross functional stakeholders across research, product & infrastructure to productize ML research
• Knowledge of large scale search & recommender systems, or modern ads ranking/retrieval/targeting systems is preferred
• Experience with deep learning, representation learning or transfer learning is preferred
• Tech lead experience in a product team is strongly preferred

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