Machine Learning Engineer - Ads Targeting

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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 staff machine learning engineer in the ads targeting quality team, you will own and execute our mission to automate targeting and deliver the most relevant audiences to advertisers under the right context with ML‑driven solutions.

• Own end-to-end design and execution of ML-based targeting products like auto targeting, user lookalikes, etc.
• Drive research direction and technical roadmap for complex projects, lead day‑to‑day project execution, and contribute meaningfully to team vision and strategy
• Be a thought leader for the team and collaborate closely with product managers and cross‑functional partners to develop and prioritize the roadmap based on data analysis, industry research and product research
• Research, implement, test, and launch new model architectures for retrieval using deep learning (GNNs, transformers, two‑tower models, LLMs) with a focus on improving advertiser outcomes
• Provide mentorship to junior MLEs
• Own offline & online experimentation of ML models for improving targeting products
• Work on large scale data systems, and product integration
• Collaborate closely with multiple stakeholders cross product, engineering, research and marketing

• Tech lead experience in a product ML team driving the research and technical direction to improve business outcomes using applied ML
• Experience with ads retrieval modeling, ranking or recommendation systems
• Experience with deep learning models for retrieval (two‑tower, GNNs, transformers, LLMs)
• + years of end-to-end experience of training, evaluating, testing, and deploying machine learning models
• + years of experience building machine learning models with Tensorflow/Pytorch
• Experience with large scale data processing & pipeline orchestration tools like Spark, Dataflow, Kubeflow, Airflow, Big Query
• Experience working with nearest‑neighbor search systems is a big plus
• Experience working with cross functional stakeholders across research, product & infrastructure to productize ML research
• Experience with deep learning, representation learning or transfer learning is preferred
• Tech lead experience in a product team is strongly preferred

• 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