Senior Machine Learning Engineer (d/f/m)

At eBay, we're 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. We're 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.

• 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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