Engineer Jobs

About The Position

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 to train and evaluate recommendation models.
• Experience developing and maintaining data pipelines, experimentation frameworks, and model training scripts using Python and related libraries such as Pandas, NumPy, and Scikit-learn.
• Experience designing and training machine learning models for ranking, personalization, and user-item relevance prediction in large-scale recommendation systems.
• Experience building and maintaining RESTful APIs to serve model predictions and integrate recommendation results into company’s buyer experience and experimentation platforms.
• Experience implementing and optimizing algorithms for candidate retrieval and ranking to improve recommendation accuracy and diversity.
• 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.
• Experience developing end-to-end recommendation pipelines including candidate generation, ranking, and post-processing to enhance personalization and engagement across company’s marketplace.
• 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 to extract insights from listing text and user queries.
• Experience building and validating predictive models to estimate user engagement metrics and forecast demand patterns for personalized recommendation strategies.
• 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.

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 (with libraries such as Pandas and NumPy) to extract meaningful user preferences and behavioral patterns.
• Develop and integrate new features into the recommendation model based on behavioral insights, leveraging domain knowledge and statistical methods.
• Implement deep learning-based recommended models using PyTorch, incorporating user and item embeddings, attention mechanisms, and custom loss functions.
• 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.

Benefits

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