AI Platform Systems Software Engineer

At eBay were more than a global ecommerce leader were changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. Were committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass authenticity thrives bold ideas are welcome and everyone can bring their unique selves to work every day. Were 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.

About the team & role:

At eBay we are building the next-generation AI platform to power experiences for millions of users worldwide. Our AI Platform (AIP) provides the scalable secure and efficient foundation for deploying and optimizing advanced machine learning and large language model (LLM) workloads at production scale. We enable teams across eBay to move from experimentation to global deployment with speed reliability and efficiency.

We are seeking an experienced AI Platform Systems Software Engineer (Infrastructure) to join our AI Platform this role you will design implement and optimize the core infrastructure that powers AI/ML workloads across eBay. You will work on highly distributed systems cloud-native services and performance-critical components that make large-scale inference and training possible.

You will be part of the team responsible for both the control plane (cluster management scheduling user access) and the data plane (execution resource allocation accelerator integration). Your work will directly impact the scalability performance and reliability of AI applications that serve eBays global marketplace.

What you will accomplish:

• Design and scale services to orchestrate AI/ML clusters across cloud and on-prem environments supporting VM and Kubernetes-based deployments including Ray () clusters for distributed training and online inference.

• Develop and optimize intelligent scheduling and resource management systems for heterogeneous compute clusters (CPU GPU accelerators).

• Integrate Ray Train/Tune for large-scale distributed training workflows and Ray Serve for low-latency autoscaled inference; build platform hooks for observability canary/A-B rollouts and fault tolerance.

• 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.

What you will bring:

• Bachelors or Masters 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 ()e.g. Ray Train/Tune for distributed training and Ray Serve for production inferencecovering autoscaling fault tolerance observability and multi-tenant operations.

• 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.

#LI-Hybrid

The base pay range for this position is expected in the range below:

Base pay offered may vary depending on multiple individualized factors including location skills and experience. The total compensation package for this position may also include other elements including a target bonus and restricted stock units (as applicable) in addition to a full range of medical financial and/or other benefits (including 401(k) eligibility and various paid time off benefits such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

If hired employees will be in an at-will position and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time including for reasons related to individual performance Company or individual department/team performance and market factors.

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