1 hour ago
Amsterdam, NetherlandsMid Level
Responsibilities
- Drive engineering delivery of live ranking and recommendation experiences across eBay surfaces.
- Develop and sustain production services, feature pipelines, model integration points, and experimentation workflows for real-time recommendations.
- Build software for recommendation ranking, candidate enrichment, feature creation, model serving, monitoring, and deployment.
- Work with large-scale distributed systems, distributed data stores, billions of daily impressions, and low-latency requirements.
- Translate product and research requirements into technical options, implementation plans, production launches, and measurable business outcomes.
- Enable A/B testing, analyze experiment results, and use findings to guide feature iterations.
- Collaborate with product, analytics, applied research, and engineering teams.
- Contribute reusable engineering patterns, documentation, and development practices.
Requirements
- Master’s degree in Computer Science or a related field with over two years of relevant experience, or a Bachelor’s degree with more than four years of relevant software engineering experience.
- Experience designing and implementing efficient, extensible, maintainable software systems in an object-oriented production language such as Java or Scala.
- Ability to translate product or research requirements into technical build options, estimates, feature specifications, and production-ready plans.
- Experience with ranking, recommendations, search, personalization, experimentation, or machine learning systems is strongly preferred.
- Experience with large-scale data pipelines, streaming or batch processing, and distributed data platforms such as Hadoop, Kafka, Spark, or Flink is advantageous.
- Experience with Python, ML pipelines, feature engineering, model integration, model evaluation, or timely LLM workflows is a plus.
- Experience with A/B testing, experiment configuration, launch evaluation, and data-guided product iteration is a plus.
- Ability to work independently, communicate assumptions and risks, and collaborate across product, analytics, applied research, and engineering groups.
- Academic papers, patents or intellectual property, and technical blogs are advantageous.
Tech Stack
Categories
