We seek exceptional machine learning engineers to join us in building a state-of-the-art machine learning system. Moloco's ML system processes over 6 million bid requests per second at under 7ms prediction latency, and our deep learning models power CTR/CVR prediction, ranking, and bid price optimization for live auction decisions at planet scale. Moloco is an engineering company founded by top-tier engineers, and machine learning is the core of Moloco's engineering systems. We understand the value of a strong engineering team and strive to hire only the best engineers.

• Develop and iterate on deep learning models for real-world prediction problems, including CTR/CVR estimation and ranking, with guidance on modeling choices and objective function design. • Build and maintain data pipelines for model training and serving using GCP products such as Dataflow, BigQuery, BigTable, and open-source frameworks such as Apache Beam, PySpark, and Iceberg. • Support production model serving, monitor model behavior in live environments, and contribute to debugging and improving model quality. • Design and run offline experiments — define evaluation metrics, test hypotheses, and document findings to contribute to team-level modeling decisions. • Collaborate with fellow Machine Learning Engineers, Applied Scientists, and Infrastructure engineers to deliver projects end-to-end within defined scopes. • Grow your understanding of Moloco's AdTech domain — including auction mechanics, bidding systems, and advertising outcome modeling — and apply that context to your work.

• Bachelor's degree or higher in Computer Science or a related technical field, or equivalent professional experience. • 2+ years of hands-on software development experience in machine learning or deep learning, with at least some exposure to production systems beyond academic or personal projects. • Working knowledge of core machine learning modeling concepts, including classification and regression model selection, loss function design, bias/variance trade-offs, calibration, and offline evaluation. • Solid foundation in statistics and probability, including conditional probability, common distributions, maximum likelihood estimation, hypothesis testing, and basic A/B test interpretation. • Experience building or contributing to data pipelines or model serving systems, with an understanding of the engineering trade-offs involved. • Proficiency in at least one programming language such as Python, Java, or Go. • Fluent English communication skills. • Senior Machine Learning Engineer

• For more details, please refer to the company website.