Reddit is home to thousands of communities, endless conversations, and authentic human connection. As our advertising business grows, delivering high-quality and relevant promoted content becomes increasingly critical to both our users and advertisers. The Engagement Modeling Team is at the heart of this effort. This team is responsible for building and maintaining machine learning models that predict and optimize user engagement with ads—ranging from click-through rates to video views and other media-driven interactions. These models power key ad-serving decisions that impact user experience, advertiser outcomes, and Reddit’s long-term business growth.
We are looking for an experienced and strategic Machine Learning Manager to lead this team. This is a high-impact, high-visibility role ideal for someone with deep machine learning expertise, a passion for building scalable systems, and the leadership skills to manage both people and projects in a dynamic, cross-functional environment.
Responsibilities
- Set Strategy and Vision: Define the technical vision and long-term roadmap for engagement modeling, aligning cutting-edge ML solutions with Reddit’s product and business objectives.
- Lead Model Development: Oversee the full ML lifecycle, from data generation and pipeline architecture to model training, evaluation, and deployment at scale.
- Drive ML Innovation: Design and implement state-of-the-art deep learning architectures focused on user engagement, ranking, and recommendation.
- Team Leadership: Build, mentor, and lead a team of talented machine learning engineers. Foster a culture of technical rigor, innovation, and career growth.
- Cross-Functional Collaboration: Work closely with product managers, data scientists, and engineers across the Ads organization to define engagement goals, model KPIs, and data needs.
- Ensure Model Quality: Maintain high standards for ML performance, scalability, interpretability, and robustness in a production environment.
Requirements
- Deep Learning Architectures: Proven expertise in building ML models for ranking, recommendation, or engagement prediction at scale.
- ML Frameworks: Proficiency with mainstream ML libraries such as TensorFlow and PyTorch.
- Production ML Systems: Experience managing the full ML lifecycle—feature engineering, training, testing, deployment, and monitoring—in a production setting.
- Data Infrastructure: Skilled in orchestrating large-scale data pipelines for training and evaluating ML models.
- Ads Modeling: Background or familiarity with engagement modeling in the ads domain (e.g., predicting user interaction with promoted content) is a strong plus.
- Team Management: Experience managing high-performing ML teams, with a focus on mentorship and team growth.
- ML Experience: Deep hands-on experience working with machine learning models in large-scale production systems.
- Cross-Functional Leadership: Strong interpersonal skills and a collaborative mindset. Able to effectively partner with PM, DS, and Ads Engineering teams to deliver impactful results.
- Strategic Thinking: Ability to develop and communicate a clear technical strategy that supports broader business objectives.
