> Markdown version of [/jobs/ext/3598781-software-engineer](https://www.wearedevelopers.com/jobs/ext/3598781-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Software Engineer

- **Company:** 0075 Ebay (uk) Limited
- **Location:** Amsterdam, Netherlands
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), A/B Testing, Artificial Intelligence, Computer Vision, Batch Processing, Distributed Data Store, Distributed Systems, Apache Hadoop, Python (Programming Language), Natural Language Processing, Object-Oriented Software Development, Recommender Systems, Software Engineering, Data Streaming, Systems Integration, Real Time Systems, Feature Engineering, Large Language Models, Apache Spark, Model Validation, Solid Principles, Information Technology, Apache Flink, Apache Kafka, Machine Learning Operations, Data Pipelines
- **Published:** October 7, 2026
- **Apply:** https://startup.jobs/software-engineer-mts-1-0075-ebay-uk-limited-10312475

## About the Role

* A Bachelor's degree in Computer Science or a related field with over five years of relevant experience in Software Engineering, or a Master's degree with over four years of relevant experience.
 * Expertise with object-oriented languages such as Java, Scala, or similar production languages in a high-scale distributed setting, building with SOLID principles.
 * Experience working with large-scale real-time systems, data pipelines, streaming or batch processing, and distributed data platforms like Hadoop, Kafka, Spark, Flink, or comparable technologies is advantageous.
 * Experience with Python, ML pipelines, feature engineering, model integration, model evaluation, or timely LLM workflows is a plus.
 * Demonstrated ability to work independently on prioritized functional areas while communicating assumptions, risks, task clarifications, and tradeoffs to collaborators upfront.
 * Experience translating product or research requirements into technical build options, estimates, feature specs, and production-ready implementation plans.
 * Experience working with search, recommendations, personalization, experimentation, or machine learning systems is strongly preferred.
 * Experience in A/B testing, experiment configuration, launch evaluation, and data-guided product iteration is a plus.
 * Previous experience publishing academic papers, patents/IP, or technical blogs is a plus.

## Description

We are currently developing innovative ranking and recommender systems utilizing the latest advancements in ML, NLP, LLMs, and AI. We are seeking a Software Engineer to help design, implement, test, and deploy highly reliable recommendation features at eBay scale. You will collaborate with product managers, engineers, and applied research leaders across our global Search & Recommendations group to build, launch, monitor, and continuously improve personalized shopping experiences.

What you will accomplish:

 * Drive engineering delivery of high-traffic search and recommendation experiences at eBay.
 * Develop and maintain large-scale production services, feature pipelines, model integration points, and experimentation workflows that support real-time and near real-time recommendations.
 * Work with large-scale distributed systems handling high-volume traffic, distributed data stores, billions of daily impressions, and strict low-latency SLA requirements.
 * Work alongside a team of applied researchers and engineers with deep expertise in recommender systems, machine-learned ranking, natural language processing, computer vision, AI/LLMs, and ML production engineering.
 * Collaborate with product, analytics, applied research, and engineering teams to transform feature requests into build options, implementation plans, production launches, and measurable business outcomes.
 * Support iterative A/B testing by enabling experiments, analyzing results, learning from outcomes, and applying those findings to drive the next feature iteration.