Senior Software Engineer

eBay
Amsterdam, Netherlands
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Amsterdam, Netherlands

Tech stack

Java
A/B testing
Artificial Intelligence
Computer Vision
Big Data
Databases
Distributed Data Store
Distributed Systems
Hadoop
Python
Machine Learning
Natural Language Processing
Recommender Systems
Cloud Services
Software Engineering
Large Language Models
Information Technology
Low Latency
Kafka
Machine Learning Operations
Key-value Store

Job description

We are building cutting edge agentic workflows and recommender systems powered by the latest ML, NLP, LLM and AI techniques. We are looking for an AI innovator who can help drive the execution of this effort forward into production at eBay scale. The candidate will work closely with leaders and other teams from our globally distributed Search & Recommendations organization, including product managers, engineers, and applied research leaders to brainstorm, build, and deliver future personalized e-commerce shopping experiences.

What you will accomplish:

  • Drive engineering delivery of recommendations experiences and agentic workflows on a variety of surfaces at eBay
  • Work with a team of applied researchers and engineers with deep expertise in natural language processing, computer vision, large language models / AI / agentic workflows, recommender systems, and ML production engineering
  • Influence how people will interact with recommender systems in the future, and how recommender systems technology will evolve
  • Work with large scale distributed systems serving real-time and near-real time traffic, distributed data stores, billions of impressions per day, and low latency SLA requirements
  • Develop and deploy state of the art AI models
  • Develop and deploy big data technology and large scale data pipelines
  • Drive marketplace GMB through iterative A/B testing

Requirements

  • MS/PhD in Computer Science or related area with 6+ years of relevant work experience (or BS/BA with 8+ years) in Software Engineering
  • Experience in production engineering practices and software development in an OO language (Scala, Java, etc.) and large scale distributed systems in an industrial setting
  • Experience with using cloud services, big data pipelines (Hadoop, Kafka, etc.) and databases (document store, key-value store, etc.)
  • Experience in Python for ML pipelines is a plus
  • Experience in industrial recommender systems or search technology is a plus
  • Previous publications experience with academic papers, patents/IP, or technical blogs is a plus

About the company

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. Looking for a company that inspires passion, courage and creativity, where you can be on the team shaping the future of global commerce? Want to shape how millions of people buy, sell, connect, and share around the world? If you're interested in joining a purpose driven community that is dedicated to crafting an ambitious and inclusive work environment, join eBay - a company you can be proud to be with. Our Search & Recommendations team works on delivering recommendations at scale and in near real time to our buyers on our website and native app platforms. Recommendations are a core part of how our buyers navigate eBay's vast and varied inventory. Our team develops state-of-the-art recommendations systems, including agentic workflows, deep learning based retrieval systems for personalized recommendations, machine learned ranking models, as well as advanced MLOps in a high volume traffic industrial e-commerce setting.

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