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About the Role

Improve the effectiveness and efficiency of the “Similar Items” recommendation module on eBay’s product pages, which directly impacts millions of daily users. Build and maintain large-scale data processing pipelines using Scala and Apache Spark, which aggregate user interaction signals such as clicks, purchases, watchlist activity, and reviews. Utilize Hadoop Distributed File System (HDFS) to store and manage terabytes of behavioral and product data. Preprocesses this data to ensure quality, completeness, and suitability for downstream machine learning tasks. Analyze structured and unstructured data using SQL and Python (with libraries such as Pandas and NumPy) to extract meaningful user preferences and behavioral patterns, including implicit feedback and negative signals. Develop and integrate new features into the recommendation model based on behavioral insights, leveraging domain knowledge and statistical methods to improve model expressiveness. Implement deep learning-based recommended models using PyTorch, incorporating user and item embeddings, attention mechanisms, and custom loss functions to optimize for relevance and diversity. Conduct both offline evaluations and online A/B testing to assess model performance. Use results to iterate and refine models based on statistical significance and business KPIs. Collaborate closely with product managers, data scientists, and backend engineers to align recommendation strategies with user experience goals and business outcomes.

Responsibilities

Improve the effectiveness and efficiency of the “Similar Items” recommendation module on eBay’s product pages.
Build and maintain large-scale data processing pipelines using Scala and Apache Spark.
Aggregate user interaction signals such as clicks, purchases, watchlist activity, and reviews.
Utilize Hadoop Distributed File System (HDFS) to store and manage terabytes of behavioral and product data.
Preprocess data to ensure quality, completeness, and suitability for downstream machine learning tasks.
Analyze structured and unstructured data using SQL and Python to extract meaningful user preferences and behavioral patterns.
Develop and integrate new features into the recommendation model based on behavioral insights.
Implement deep learning-based recommended models using PyTorch.
Conduct offline evaluations and online A/B testing to assess model performance.
Collaborate closely with product managers, data scientists, and backend engineers.

Requirements

Master’s degree, or foreign equivalent, in Computer Science, Engineering (any field), or a closely related field.
Experience writing complex SQL queries to extract, join, and aggregate user behavior and item metadata from large-scale relational databases.
Experience developing and maintaining data pipelines, experimentation frameworks, and model training scripts using Python and related libraries.
Experience designing and training machine learning models for ranking, personalization, and user-item relevance prediction.
Experience building and maintaining RESTful APIs to serve model predictions and integrate recommendation results.
Experience implementing and optimizing algorithms for candidate retrieval and ranking.
Experience utilizing efficient data structures to handle large-scale user-item interaction data and accelerate model computation.
Experience developing end-to-end recommendation pipelines including candidate generation, ranking, and post-processing.
Experience leveraging large language models for semantic understanding of item titles and descriptions, query expansion, and improving cold-start recommendations.
Experience applying NLP techniques such as embeddings, text classification, and semantic similarity modeling.
Experience building and validating predictive models to estimate user engagement metrics and forecast demand patterns.
Experience using Hadoop and Spark to process terabytes of behavioral and transactional data, performing ETL.
Must be legally authorized to work in the U.S. without sponsorship.

Skills

SQL Queries
Python Programming
Machine Learning
API Development
Algorithm Development
Data Structures
Recommendation Systems
LLMs (Large Language Models)
NLP (Natural Language Processing)
Predictive Modeling
Hadoop & Spark Big Data Processing ETL
Scala
Apache Spark
Hadoop Distributed File System (HDFS)
Pandas
NumPy
Scikit-learn
PyTorch

Location

New York, NY

Work Type

Partial telecommuting permitted

Experience Level

Software Engineer 2

Education Level

Master’s degree

Salary/Compensations

$131,997.00-229,600.00 per annum

Benefits

target bonus
restricted stock units
full range of medical, financial, or other benefits
401(k) eligibility
various paid time off benefits
PTO
parental leave

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.

Equal Opportunity

eBay is an equal opportunity employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status.
If you have a need that requires accommodation, please contact us at talent@ebay.com.
We will make every effort to respond to your request for accommodation as soon as possible.
View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities.