Make intelligence open and accessible to all. Backed by Battery, Lightspeed and Sequoia.
About the role
Reflection’s Compute Platform team specializes in keeping our compute layer healthy and highly available. We run a K8s-based platform distributed across multiple neo-clouds. We manage multi-cloud scheduling, node health, and performance debugging at this scale presents genuinely hard systems problems. More broadly, you will work closely with Reflection's training teams to co-design fault tolerance, node health checks, and remediation strategies.
What they're looking for
- • Systems-level engineering experience with a focus on cluster-wide behavior and maintenance
- • Strong coding ability and a demonstrated focus on systems or GPU infrastructure
- • Deep GPU hardware knowledge beyond standard Kubernetes,e.g., familiarity with NCCL
- • Alignment with a K8s-first architecture
- • Cloud storage expertise, specifically managing high-performance data products (like VAST) across multiple data centers, connecting those storage environments together and handling datasets and checkpointing at scale
More about this role
Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
Reflection’s Compute Platform team specializes in keeping our compute layer healthy and highly available. We run a K8s-based platform distributed across multiple neo-clouds. We manage multi-cloud scheduling, node health, and performance debugging at this scale presents genuinely hard systems problems. More broadly, you will work closely with Reflection's training teams to co-design fault tolerance, node health checks, and remediation strategies.
Cluster Management: Build and maintain tools for the automatic remediation, topology-aware scheduling, capacity planning and rapid hardware debugging.
Platform Engineering: Design and iterate on our cluster management stack for workloads across large, multi-GPU fleets
Monitoring & Observability: Implement comprehensive cluster-wide monitoring, focusing on durability and active performance benchmarking.
Roadmap Execution: Prepare the infrastructure for next-generation GPU...
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