Machine Learning Engineer, Ads

Reddit is a community-driven platform that fosters open conversations and engagement among its users. They are seeking a Machine Learning Engineer to contribute to the Ads team, focusing on the lifecycle of machine learning systems to enhance ad relevance and deliver value to advertisers.

Responsibilities:

• Design, build, and deploy industrial-level machine learning models to solve critical problems in ad ranking, bidding, and optimization
• Take full ownership of the ML lifecycle, from ideation and research to building scalable serving systems and maintaining models in production
• Perform systematic feature engineering to transform raw, diverse data into high-quality features that drive model performance
• Work closely with product managers, data scientists, and engineers to translate business challenges into effective ML solutions
• Improve the reliability and stability of our ML systems by building robust monitoring, alerting, and automated retraining pipelines
• Research new algorithms, stay up-to-date with state-of-the-art ML techniques, and contribute to the team’s strategy and roadmap

Requirements:

• At least 3+ years of end-to-end experience in training, evaluating, and deploying machine learning models in a production environment
• Proficient in one or more general-purpose programming languages (e.g., Python, Scala) and have a solid understanding of software development best practices
• Hands-on experience with a major machine learning framework (e.g., TensorFlow, PyTorch) and a deep understanding of core ML concepts and algorithms
• Proven ability to work effectively with cross-functional teams, including product managers and data scientists, to translate business needs into technical solutions
• Track record of using machine learning to drive key performance indicator (KPI) wins and solve complex, real-world problems
• Experience working in the Ads domain
• Experience or interest in the advertising business and understanding customer needs
• An advanced degree (MS/PhD) in a quantitative field
• Familiarity with distributed systems and large-scale data processing technologies (e.g., Spark, Kafka)