This role is no longer listed.eBay took the posting down on 21 Oct, so applying now won't reach anyone. It stays here for your saved list and any links you shared.
SWE-3 Backend Engineer, ML Systems
eBay (company site, opens in a new tab)4+ yrsBengaluruOn-siteDirect career pageEst. 25–45 LPA
Posted on 8 Sept
Reach people at eBay
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at eBay you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, build, and operate scalable backend and MLOps systems supporting sponsored experiences, ranking, retrieval, and personalization at eBay scale.
- Develop and maintain CI/CD pipelines, data pipelines, and APIs to enable production-grade ML models, including Generative AI models with low latency and high throughput.
- Collaborate with Applied Researchers and Product teams to translate prototype ML models into robust production systems and monitor their performance.
Minimum Requirements
- Bachelor's degree in Computer Science or related field.
- 4+ years of relevant work experience in ML, AI, or Data Engineering.
- Experience with big data distributed processing frameworks (e.g., Apache Hadoop, Spark, Flink) and ML frameworks like TensorFlow and PyTorch in production.
- Experience building and managing CI/CD pipelines for ML models, including containerization (Docker, Kubernetes), cloud services (AWS, GCP, Azure), and expertise in designing scalable distributed systems with REST/gRPC APIs.
Ideal Candidate Profile
- Experienced backend engineer skilled at bridging machine learning research and production software engineering.
- Strong background in engineering data pipelines and MLOps for continuous integration, delivery, and model monitoring at large scale.
- Comfortable working with state-of-the-art Generative AI technologies and collaborating cross-functionally with researchers and product managers to build scalable ML-powered ecommerce experiences.