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.
eBay, Inc. seeks Applied Researcher 2.
Job Duties: Develop advanced feature learning models for eBay’s search engine, enhancing its efficiency and effectiveness. Play a pivotal role in personalizing the ranking system, contributing to a more tailored user-centric experience. Conduct research on the application of LLM in search and ranking to drive innovative solutions. Seek scientifically valid solutions based on the data in eBay. Build deep learning models to deliver insightful and practical solutions. Build big data pipeline for data processing using Hadoop and Apache Spark. Apply natural language processing techniques to extract meaningful insights from text data in eBay. Apply data mining methods to analyze large amount data in eBay. Apply statistical principles to the collection, analysis, and validation of data. Partial telecommuting may be permitted from within a commutable distance.
Minimum Requirements: PhD degree, or foreign equivalent, in Computer Science, Engineering, or a closely related field plus one year of experience in the job offered or a related occupation. Employer will accept a Master’s degree, or foreign equivalent, in Computer Science, Engineering or a closely related field plus three years of experience in the job offered or a related occupation.
Special Skill Requirements:
1. Develop advanced machine learning models using deep learning frameworks like TensorFlow.
2. Build big data pipeline to efficiently process large volumes of data.
3. Build machine learning models for time series data.
4. Conduct research on the application of different machine learning models, such as sequence model, transformer model, and reinforcement learning model etc.
5. Utilize natural language processing techniques to extract meaningful insights from text data.
6. Apply data mining methods to analyze extensive datasets and uncover valuable trends and patterns.
7. Implement statistical analysis methods and experimental design to ensure scientifically valid solutions.
8. Integrate machine learning applications into the production environment.
9. Write research papers on findings and innovations.