AI Scientist
Xaira Therapeutics
PyTorch
Machine Learning Engineer, Ads
Reddit, Inc.
full-time
Posted on:
Location: 🇺🇸 United States
Visit company websiteSalary
💰 $185,800 - $260,100 per year
Job Level
Mid-LevelSenior
Tech Stack
Distributed SystemsKafkaPythonPyTorchScalaSparkTensorflow
About the role
- Join the Ads team as a Machine Learning Engineer and become a key contributor to Reddit’s business. In this hands-on role, you will be responsible for the full lifecycle of our ML systems, from initial research and modeling to deployment and optimization in production. Your work will directly impact how we deliver relevant ads and drive value for our advertisers across areas like ad ranking, bidding, measurement, and optimization.
- 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.
