Senior Machine Learning Engineer

At eBay, were more than a global ecommerce leader we re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work every day. Were in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers and help us connect people and build communities to create economic opportunity for all. Do you love data Graphs Machine Learning Knowledgeable, collaborative co-workers Come work at eBay and help us redefine global, online commerce! What Will You Do We are looking for a Lead Machine Learning Engineer to build and scale production ML systems that power customer-facing experiences. You will work across the full lifecycle from integrating models provided by research team into production to designing scalable, reliable ML systems. You will be responsible for delivering production-ready ML solutions, working closely with researchers and platform teams to ensure reliability, scalability, and operational excellence. As a senior member, you will mentor engineers, drive technical decisions, and lead complex projects to completion. Your work will include: Converting research models into production-ready systems integrated with pipelines and services Designing scalable ML systems for batch use cases Building data pipelines for ML workflows , that includes basic use of data processing tools like Spark Defining model monitoring and reliability standards Establishing model lifecycle practices (versioning, rollback, retraining) Job Responsibilities Productionize ML models and integrate them into pipelines. Design and maintain ML pipelines for feature generation and inference Work with large-scale datasets and distributed processing frameworks (e.g., Spark) Define model health metrics, monitoring, and alerting strategies Architect ML systems that meet latency, throughput, and scalability requirements Drive code quality, reusability, and engineering best practices Optimize systems for cost, performance, and efficiency Collaborate with Applied Research to adapt models for production constraints Mentor engineers on ML engineering and system design best practices Minimum Qualifications Strong experience in machine learning engineering and production ML systems Bachelor s or Master s degree in Computer Science, Machine Learning, or related field 9+ years of experience building ML or data-driven systems Strong foundations in ML system design. Strong programming experience in Python Working knowledge of SQL and basic Spark / PySpark for data processing Experience with model serving, MLOps, and monitoring systems Proven ability to lead projects and mentor team members Strong communication and collaboration skills Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.