Machine Learning Engineer (머신러닝 엔지니어)

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.

Responsibilities

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

Requirements

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; 5+ years of hands-on software development experience in machine learning and deep learning, with a clear focus on production systems rather than research prototyping; 8+ years of hands-on software development experience in machine learning and deep learning, with a demonstrated focus on large-scale production systems; 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; Strong machine learning modeling depth, including model selection for classification, regression, and ranking, loss function design, calibration, class imbalance handling, and bias/variance trade-off reasoning; Expert-level machine learning modeling depth, including objective function design, calibration, multi-task learning, ranking, and the ability to reason about trade-offs across the full modeling lifecycle; Solid foundation in statistics and probability, including conditional probability, common distributions, maximum likelihood estimation, hypothesis testing, and basic A/B test interpretation; Solid foundation in statistics and probability, including Bayesian inference, maximum likelihood estimation, hypothesis testing, A/B experiment design and interpretation, and probabilistic reasoning under uncertainty; Strong foundation in statistics and probability, including Bayesian inference, causal reasoning, experiment design, and the ability to design and interpret complex A/B tests with confidence; Experience building or contributing to data pipelines or model serving systems, with an understanding of the engineering trade-offs involved; Demonstrated experience designing and operating large-scale machine learning systems under real-world constraints, including model-serving architectures, feature stores, and training pipelines; Deep experience designing and operating large-scale machine learning systems end-to-end under real-world constraints, including model serving architectures, feature pipelines, retraining strategies, and observability; Proficiency in at least one programming language such as Python, Java, or Go; Proficiency in at least one programming language such as Python, Java, or Go, with the ability to write clean, robust, production-quality code; Proficiency in at least one programming language, such as Python, Java, or Go, with the ability to write and review production-quality code as part of a team; Fluent English communication skills; Fluent English communication skills, including the ability to align technical and non-technical stakeholders at the leadership level; Proven track record of driving technical direction across teams or domains — not just executing projects, but defining the approach, resolving ambiguity, and influencing how others solve problems

Nice to have

Relevant experience in AdTech; MS or Ph.D. degree in Computer Science or related technical field