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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.
As an ML Engineer at eBay's Risk team, you'll be part of an exciting journey that is instrumental in maintaining eBay to be a safe and trusted marketplace. The role involves working on critical projects related to the Risk product, which enhances the model lifecycle management for applied researchers, data scientist and ML engineers like yourself. This team is known for fostering an innovative culture and a collaborative environment, encouraging continuous learning and growth. The Risk Engineering team is a key player in keeping the marketplace safe for all buyers and seller.
What will you accomplish?AI/ML EngineeringDesign and build machine learning models to detect fraud, bot attacks, collusion etc.
Perform feature engineering, model development, evaluation, and optimization for high-accuracy ML applications.
Fine-tune and implement Deep Neural Network (DNN) architectures.
Construct robust ML pipelines for training, validation, and deployment using modern ML stacks.
Apply prompt engineering techniques with Generative AI models (LLMs, diffusion models, etc.) to tackle application-driven problems.
Leverage vector databases and build/optimize embeddings for search, retrieval, and semantic understanding.
Lead efforts in simulation, synthetic data generation, and experimentation.
Build reliable APIs and services that expose ML model outputs for real-time decisioning.
Evaluate bias and fairness across population subgroups.
Maintain logging, tracing, and alerting for model inputs/outputs, feature importance, versions, and pipeline steps.
Lead and participate in data validation, preprocessing, and cleansing workflows to ensure ML readiness.
Work closely with engineers, product managers, and collaborators to develop scalable ML-powered applications.
What will you bring?
At least 5 years of experience in building AI/ML-based products and solutions in production environments.
A solid foundation in Data Structures, Algorithms, Object-Oriented Programming, Software Design, and core Statistics knowledge
Proven expertise in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch, Keras.
Deep understanding of machine learning fundamentals, algorithms, and model evaluation techniques.
Hands-on experience with ML Ops tools and best practices.
Experience with OCR, NLP, vector search, embeddings, and LLM-based applications.
Experience in the close examination of data and computation of statistics
Proficiency in working with large scale data in hadoop and spark.
Proficient with prediction accuracy, latency, throughput, confidence scores, and drift (data & concept).
Strong programming, system design, and debugging skills.
Experience working in domains such as fraud detection, credit risk, compliance, advertising, or recommendations is highly preferred.
Publication of research papers or technical articles in ML conferences or journals is highly desirable.
Education: Bachelor’s or Master’s in CS, Engineering, Math, or related field; PhD preferred but not required.
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