Machine Learning Engineer II, Infrastructure (머신러닝 엔지니어 II - 인프라)

What you'll do

• Design, build, and maintain robust machine learning infrastructure to support large-scale ad serving and model training globally
• Develop and optimize data pipelines and workflows for efficient model deployment and monitoring
• Collaborate with cross-functional teams including data scientists, product managers, and software engineers to deliver end-to-end ML solutions
• Implement best practices for model versioning, reproducibility, and CI/CD in ML systems
• Build and operate high-performance ML systems using frameworks and languages such as JAX and Rust, optimized for GPUs and TPUs
• Monitor, troubleshoot, and continuously improve the reliability, scalability, and performance of ML systems delivering millions of predictions per second worldwide
• Evaluate and integrate new tools, frameworks, and technologies to enhance the ML platform’s capabilities
• Integrate AI-driven agents into the core engineering and modeling lifecycle to automate and amplify the team's impact
• Contribute to the design and execution of experiments to improve ad quality and system performance
• Document system architecture, processes, and best practices to ensure knowledge sharing and maintainability

Requirements

• 4+ years of experience in machine learning engineering, ML infrastructure, or a related field
• Proficiency in programming languages such as Python, Java, C++, or Rust
• Hands-on experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, Jax)
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration tools (e.g., Docker, Kubernetes)
• Experience building and maintaining scalable data pipelines using tools such as Apache Beam, Apache Spark, Airflow
• Ability to thrive in ambiguous environments and proactively solve complex infrastructure challenges
• Strong collaboration skills with cross-functional teams
• Strong problem-solving skills and a growth mindset

Tech stack

Benefits