Software Engineer, Platform Infrastructure
Builds and scales control-plane and data-plane infrastructure for distributed AI workloads, including Ray cluster orchestration, scheduling, Kubernetes deployments, and accelerator integration. Requires 3+ years of production software experience, distributed-systems expertise, and proficiency in Go and Python.
About the job
Responsibilities
- Design, build, and scale services that orchestrate Ray clusters across cloud and on-premises environments, supporting VM-based and Kubernetes-based deployments.
- Optimize control-plane components for large-scale distributed AI/ML workloads.
- Build intelligent scheduling and resource-management systems for heterogeneous compute clusters.
- Develop features that improve the reliability, performance, scalability, and observability of managed Ray workloads.
- Support and optimize accelerator integration, including GPUs and TPUs.
- Handle container image management and dependency resolution for distributed workloads.
- Participate in code reviews and design and architecture discussions.
- Provide on-call support and collaborate with customer and field teams to troubleshoot infrastructure issues.
- Collaborate with distributed-systems and machine-learning experts to advance AI infrastructure.
Requirements
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
- 3+ years of experience writing high-quality production code.
- Hands-on experience building and maintaining highly available, scalable, and performant distributed systems.
- Expertise in cloud-native technologies and Kubernetes-based deployments.
- Strong understanding of networking, security, and authentication mechanisms in cloud environments.
- Familiarity with observability stacks such as Prometheus and Grafana.
- Proficiency in Go and Python.
- Knowledge of low-level operating-system foundations, including the Linux kernel, file systems, and containers.
Skills
Ray, Kubernetes, AWS, Azure, GCP, Go, Python, Prometheus, Grafana, Linux, Containers, Distributed Systems, Networking, Cloud Security, Gpus
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