Machine Learning Engineer - Ads Targeting

Position: Staff 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.

Your Responsibilities :

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

Required Qualifications

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

Benefits:

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