Moloco
Technology
MachineLearningEngineerII,Infrastructure
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning Engineer II, Infrastructure at Moloco. Skills: Machine learning, ML infrastructure, Data pipelines, System performance. Drive development of ML infrastructure. Optimize scalable ML infrastructure”
What You'll Achieve.
Deliver high-quality solutions; Deliver data-driven solutions; Deliver solutions at scale; Deliver millions of predictions per second
Industry & Context.
Problem-solving skills; Troubleshoot ML systems; Solve complex infrastructure challenges
What They're Looking For.
Must Have
4+ years experience ML engineering, 4+ years experience ML infrastructure, Proficiency in Python, Proficiency in Java, Proficiency in C++, Proficiency in Rust, Hands-on experience with TensorFlow, Hands-on experience with PyTorch, Hands-on experience with Keras, Hands-on experience with Jax, Work with AWS, Work with GCP, Work with Azure, Build scalable data pipelines, Maintain scalable data pipelines, Use Apache Beam, Use Apache Spark, Use Airflow, Solve complex infrastructure challenges, Collaborate with cross-functional teams
Nice to Have
Experience with containerization, Experience with orchestration tools
What You'll Do.
Drive development of ML infrastructure
Optimize scalable ML infrastructure
Enhance performance of Ads product
Enhance reliability of Ads product
Design ML infrastructure
Build ML infrastructure
Maintain ML infrastructure
Support large-scale ad serving
Support model training globally
Develop data pipelines
Optimize data pipelines
Collaborate with data scientists
Collaborate with product managers
Collaborate with software engineers
Deliver end-to-end ML solutions
Implement model versioning
Implement reproducibility
Implement CI/CD in ML systems
Build high-performance ML systems
Operate high-performance ML systems
Troubleshoot ML systems
Improve ML system reliability
Improve ML system scalability
Improve ML system performance
Evaluate new frameworks
Integrate new frameworks
Evaluate new technologies
Integrate new technologies
Enhance ML platform capabilities
Automate engineering lifecycle
Improve system performance
Document system architecture
Document best practices
How You'll Work.
Team & Collaboration
Cross-functional teams
Full Job Description
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. Moloco is headquartered in Silicon Valley, with offices in Seattle, New York, San Francisco, Seoul, Beijing, Singapore, Gurgaon, Tokyo, Shanghai, London, Tel Aviv, and Berlin. Moloco is a truly rewarding place to work and in an exciting period of growth, which you could be a part of. Join us today and apply now! 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-en
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