Machine Learning Engineer II, Infrastructure

About Moloco:

Moloco builds some of the most powerful AI advertising solutions in the world. Our name—short for "machine learning company"—reflects our core mission: democratizing access to the advanced AI that has historically been reserved for tech giants. Led by machine learning pioneers who built some of the most successful ad systems at Google, including YouTube's monetization engine and key search advertising technologies, we're transforming how businesses grow and compete in the digital economy. Built with AI from day one, Moloco’s planet-scale machine learning platform powers a suite of solutions for advertising growth and monetization. Moloco Ads is an AI-powered platform that delivers real business outcomes for mobile app marketers through performance-based user acquisition. Moloco Commerce Media enables retailers and marketplaces to build revenue-generating ad businesses that balance user experience and advertiser performance.

The Impact You’ll Be Contributing to Moloco

As a Machine Learning Engineer focused on ML Infra, you will drive the development and optimization of scalable machine learning infrastructure, directly enhancing the performance and reliability of our Ads product line. Your work will empower teams to deliver high-quality, data-driven advertising solutions at scale.

The Opportunity

• 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 continuous integration/continuous deployment (CI/CD) in ML systems.
• Build and operate high-performance ML systems using modern frameworks and languages such as JAX and Rust, optimized for execution on 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.

How Do I Know if the Role is Right For Me?

• 4+ years of experience in machine learning engineering, ML infrastructure, or a related field.
• Demonstrate proficiency in programming languages such as Python, Java, C++, or Rust.
• Have hands-on experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, Jax).
• Work with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration tools (e.g., Docker, Kubernetes).
• Build and maintain scalable data pipelines using tools such as Apache Beam, Apache Spark, Airflow, and similar platforms.
• Thrive in ambiguous environments, proactively identifying and solving complex infrastructure challenges.
• Collaborate effectively with cross-functional teams to deliver impactful solutions.
• Apply strong problem-solving skills and a growth mindset to continuously improve systems and processes.