Ebay Senior Machine Learning Engineer d/f/m Germany, Brandenburg 431306096
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We are looking for exceptional Engineers, who take pride in creating simple solutions to apparently-complex problems. Our Engineering tasks typically involve at least one of the following:
Building a pipeline that processes up to billions of items; frequently employing ML models on these datasets
Creating services that provide Search or other Information Retrieval capabilities at low latency on datasets of hundreds of millions of items
Crafting sound API design and driving integration between our Data layers and Customer-facing applications and components
Designing and running A/B tests in Production experiences in order to vet and measure the impact of any new or improved functionality
Design, deliver, and maintain significant features in data pipelines, ML processing, and / or service infrastructure
Optimize software performance to achieve the required throughput and / or latency
Work with your manager, peers, and Product Managers to scope projects and features
Come up with a sound technical strategy, taking into consideration the project goals, timelines, and expected impact
Take point on some cross-team efforts, taking ownership of a business problem and ensuring the different teams are in sync and working towards a coherent technical solution
Take active part in knowledge sharing across the organization - both teaching and learning from others
B.Sc. or M.Sc. in Computer Science or an equivalent professional experience
7+ years of software design and development experience, tackling non-trivial problems in backend services and / or data pipelines
Full proficiency in Python; additional hands-on experience with Java is a plus!
Solid foundation in Computer Science with strong proficiencies in Data Structures, Algorithms, Object-Oriented Programming, and Software Design
Experience in designing and operating Big Data processing pipelines, such as: Hadoop, Spark, Hive
Track record of impactful publications and/or patents in machine learning or related areas.
Contributions to open-source ML tools or frameworks.
Experience with modern large language models, graph-based ML, or knowledge graph construction.
Strong presence in scientific communities through talks, panels, or organizing roles.
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