About the Role

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.

As a Machine Learning Engineer, you will contribute to the full machine learning lifecycle — from model development and experimentation to data pipeline maintenance and production deployment. This role is designed for engineers who have solid machine learning and software engineering fundamentals, can execute end-to-end tasks with increasing independence, and are eager to grow through hands-on work in one of the most technically demanding real-time ML environments in the industry.

What You Will Do

• 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.

Basic Qualifications

• MS or Ph.D. degree in Computer Science or related technical field
• 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.