Machine Learning Engineer II

Build batch and real-time ML pipelines for ad delivery, ranking, bidding, and optimization Deploy and integrate ML models into low-latency ad delivery and campaign systems Develop and maintain data pipelines for large-scale impression, click, and conversion data Create reusable ML components, APIs, and workflows to support experimentation and iteration Ensure reliability, scalability, and performance through monitoring and continuous optimization Analyze data to improve models, features, and business outcomes Collaborate with product, engineering, and analytics teams to deliver marketplace impact Apply software engineering best practices and support responsible use of AI/ML Own and deliver ML components or services within cross-functional teams Mentor others and help set technical direction and engineering standards Requirements Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience 2+ years of industry experience in machine learning engineering, including model development and production deployment Proficiency in Python or Java Experience using machine learning frameworks Solid software engineering fundamentals Proven ability to own and deliver ML components or services within cross-functional teams Familiarity with real-time or near-real-time ML systems and production operations Experience operating production ML systems at scale, including monitoring, troubleshooting, and iterative improvement Familiarity with distributed data processing, including Spark and Databricks Familiarity with cloud platforms, preferably AWS Exposure to MLOps practices, including model deployment, monitoring, retraining, and CI/CD for ML Experience building or experimenting with ranking, prediction, classification, recommendation, or NLP models Experience designing APIs, data pipelines, and data models for ML-powered applications Interest or background in advertising, marketplaces, e-commerce, or travel platforms Awareness of responsible and secure AI/ML practices in production environments Employees must be able to work in the office at least three days a week No relocation assistance is available Core Competencies Demonstrates expertise in building and deploying machine learning pipelines, with a strong foundation in software engineering principles and experience in real-time ML systems. Capable of collaborating across teams to enhance model performance and business outcomes while adhering to responsible AI/ML practices. Highest-signal resume keywords Machine Learning Engineering Python or Java Proficiency MLOps Practices Distributed Data Processing Cloud Platforms (AWS) ATS Optimization Keywords Hard Skills Machine Learning Frameworks Data Pipeline Development API Design Model Deployment Monitoring and Troubleshooting Ranking Models Prediction Models Classification Models Recommendation Models Natural Language Processing (NLP) Soft Skills Collaboration Mentoring Technical Direction Industry Keywords Advertising Marketplaces E-commerce Travel Platforms Tools & Technologies Spark Databricks AWS #J-18808-Ljbffr