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Improve the effectiveness of the 'Similar Items' recommendation module by building large-scale data pipelines and implementing deep learning models. Collaborate with cross-functional teams to align recommendation strategies with user experience and business KPIs.
Requires a Master's degree in Computer Science or Engineering with expertise in Python, SQL, and big data processing using Spark and Hadoop. Must have experience designing end-to-end recommendation systems and applying NLP and LLM techniques.
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.
About the team and the role:
eBay, Inc. seeks Software Engineer 2 in New York, NY
What you will accomplish:
Job Duties: 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. Partial telecommuting permitted from within a commutable distance.
What you will bring:
Minimum Requirements: Master’s degree, or foreign equivalent, in Computer Science, Engineering (any field), or a closely related field.
Special Skill Requirements:
1. SQL Queries - Experience writing complex SQL queries to extract, join, and aggregate user behavior and item metadata from large-scale relational databases to train and evaluate recommendation models.
2. Python Programming - Experience developing and maintaining data pipelines, experimentation frameworks, and model training scripts using Python and related libraries such as Pandas, NumPy, and Scikit-learn.
3. Machine Learning - Experience designing and training machine learning models for ranking, personalization, and user-item relevance prediction in large-scale recommendation systems.
4. API Development - Experience building and maintaining RESTful APIs to serve model predictions and integrate recommendation results into company’s buyer experience and experimentation platforms.
5. Algorithm Development - Experience implementing and optimizing algorithms for candidate retrieval and ranking to improve recommendation accuracy and diversity.
6. Data Structures - Experience utilizing efficient data structures (e.g., hash maps, heaps, trees, sparse matrices) to handle large-scale user-item interaction data and accelerate model computation.
7. Recommendation Systems - Experience developing end-to-end recommendation pipelines including candidate generation, ranking, and post-processing to enhance personalization and engagement across company’s marketplace.
8. LLMs (Large Language Models) - Experience leveraging large language models for semantic understanding of item titles and descriptions, query expansion, and improving cold-start recommendations.
9. NLP (Natural Language Processing) - Experience applying NLP techniques such as embeddings, text classification, and semantic similarity modeling to extract insights from listing text and user queries.
10. Predictive Modeling - Experience building and validating predictive models to estimate user engagement metrics and forecast demand patterns for personalized recommendation strategies.
11. Hadoop & Spark Big Data Processing ETL - Experience using Hadoop and Spark to process terabytes of behavioral and transactional data, performing ETL to prepare features and datasets for large-scale machine learning training and evaluation.
Salary: $131,997.00-229,600.00 per annum. 40 hours per week; M-F, 9:00 a.m. to 5:00 p.m.
Must be legally authorized to work in the U.S. without sponsorship.
Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience. The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable) in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
If hired, employees will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
Remote roles are not eligible for U.S. visa sponsorship.
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.
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We connect people and build communities to create economic opportunity for all.
At eBay, we create pathways to connect millions of sellers and buyers in more than 190 markets around the world. Our technology empowers our customers, providing everyone the opportunity to grow and thrive — no matter who they are or where they are in the world. And the ripple effect of our work creates waves of change for our customers, our company, our communities and our planet.
Offices: 2025 Hamilton Avenue, San Jose, CA 95125, US
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