Requirements:

• Drive engineering delivery of high-traffic search and recommendation experiences at eBay.
• Develop and maintain large-scale production services, feature pipelines, model integration points, and experimentation workflows that support real-time and near real-time recommendations.
• Work with large-scale distributed systems handling high-volume traffic, distributed data stores, billions of daily impressions, and strict low-latency SLA requirements.
• Work alongside a team of applied researchers and engineers with deep expertise in recommender systems, machine-learned ranking, natural language processing, computer vision, AI/LLMs, and ML production engineering.
• Collaborate with product, analytics, applied research, and engineering teams to transform feature requests into build options, implementation plans, production launches, and measurable business outcomes.
• Support iterative A/B testing by enabling experiments, analyzing results, learning from outcomes, and applying those findings to drive the next feature iteration.

What you will bring:

• A Bachelor’s degree in Computer Science or a related field with over five years of relevant experience in Software Engineering, or a Master’s degree with over four years of relevant experience.
• Expertise with object-oriented languages such as Java, Scala, or similar production languages in a high-scale distributed setting, building with SOLID principles.
• Experience working with large-scale real-time systems, data pipelines, streaming or batch processing, and distributed data platforms like Hadoop, Kafka, Spark, Flink, or comparable technologies is advantageous.
• Experience with Python, ML pipelines, feature engineering, model integration, model evaluation, or timely LLM workflows is a plus.
• Demonstrated ability to work independently on prioritized functional areas while communicating assumptions, risks, task clarifications, and tradeoffs to collaborators upfront.
• Experience translating product or research requirements into technical build options, estimates, feature specs, and production-ready implementation plans.
• Experience working with search, recommendations, personalization, experimentation, or machine learning systems is strongly preferred.
• Experience in A/B testing, experiment configuration, launch evaluation, and data-guided product iteration is a plus.
• Previous experience publishing academic papers, patents/IP, or technical blogs is a plus.

What you'll be doing:

• Drive engineering delivery of high-traffic search and recommendation experiences at eBay.
• Develop and maintain large-scale production services, feature pipelines, model integration points, and experimentation workflows that support real-time and near real-time recommendations.
• Work with large-scale distributed systems handling high-volume traffic, distributed data stores, billions of daily impressions, and strict low-latency SLA requirements.
• Work alongside a team of applied researchers and engineers with deep expertise in recommender systems, machine-learned ranking, natural language processing, computer vision, AI/LLMs, and ML production engineering.
• Collaborate with product, analytics, applied research, and engineering teams to transform feature requests into build options, implementation plans, production launches, and measurable business outcomes.
• Support iterative A/B testing by enabling experiments, analyzing results, learning from outcomes, and applying those findings to drive the next feature iteration.

Perks and benefits:

• Joining a team of passionate thinkers, innovators, and dreamers.
• Helping connect people and build communities to create economic opportunity for all.
• Contributing to the future of global commerce.