Machine Learning Engineer, Infrastructure

Moloco•Menlo Park, CA

3h•$167,200 - $210,000

About The Position

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.

Requirements

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 like Apache Beam, Apache Spark, Airflow, or similar.
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.

Responsibilities

Design, build, and maintain robust machine learning infrastructure to support large-scale ad serving and model training at a global scale.
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.