Senior AI Engineer contributing to the technical architecture and implementation of multi - agent AI systems at Chip. Collaborating with teams to build and scale AI infrastructure for customer - facing products.
About the role
- AI Platform Systems Software Engineer responsible for designing core infrastructure for AI/ML workloads. Join eBay in building a next-generation AI platform for millions of users.
Responsibilities
- Design and scale services to orchestrate AI/ML clusters across cloud and on-prem environments
- Develop and optimize intelligent scheduling and resource management systems for heterogeneous compute clusters
- Integrate Ray Train/Tune for large-scale distributed training workflows and Ray Serve for low-latency, autoscaled inference
- Build features to improve reliability, performance, observability, and cost-efficiency of AI workloads at scale
- Enhance the control plane to support secure multi-tenancy and enterprise-grade governance
- Implement systems for container management, dependency resolution, and large-scale model distribution
- Collaborate with ML researchers, applied scientists, and distributed systems engineers to drive platform innovation
- Provide production support and work closely with field teams to resolve infrastructure issues
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience)
- 8-10 years of experience building and maintaining infrastructure for highly available, scalable, and performant distributed systems
- Proven expertise with cloud-native technologies (AWS, GCP, Azure) and Kubernetes-based deployments
- Hands-on experience running ML training and inference with Ray (ray.io)
- Deep understanding of networking, security, authentication, and identity management in distributed/cloud environments
- Hands-on experience with observability stacks (Prometheus, Grafana, OpenTelemetry, etc.)
- Strong coding skills in Go and/or Python; familiarity with other systems-level languages is a plus
- Knowledge of Linux internals, containers, and storage systems
- Experience optimizing for GPU/accelerator integration (NVIDIA, AMD, TPU, etc.) is highly desirable
Benefits
- Full range of medical benefits
- Financial benefits
- Various paid time off benefits, such as PTO and parental leave
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