Platform Engineer: Multi-Cloud GPU Clusters & K8s

reflectionai; New York (NY); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

Top-tier compensation

Stock options

Health & wellness benefits

Meals provided in office

22 weeks parental leave

Unlimited vacation in US, 30 days inUK

Visa sponsorship

Job summary

Reflection is building a Kubernetes-first, multi-cloud compute platform to enable scalable GPU workloads and fault-tolerant operations. You will help design and maintain tooling for automatic remediation, capacity planning, and performance debugging across large GPU fleets.

The role emphasizes integrating with training teams to co-design fault tolerance and remediation strategies, while aligning with a multi-cloud storage and data replication roadmap.

Qualifications

• Systems-level engineering experience focusing on cluster-wide behavior.
• Strong coding ability with a focus on systems or GPU infrastructure.
• Deep GPU hardware knowledge beyond standard Kubernetes (NCCL familiarity).
• Alignment with a K8s-first architecture.
• Cloud storage expertise across multi-data-center environments and data replication.

Responsibilities

• Cluster Management: Build and maintain tools for automatic remediation, topology-aware scheduling, capacity planning and rapid hardware debugging.
• Platform Engineering: Design and iterate on the cluster management stack for workloads across large, multi-GPU fleets.
• Monitoring & Observability: Implement comprehensive cluster-wide monitoring, focusing on durability and performance benchmarking.
• Roadmap Execution: Prepare the infrastructure for next-generation GPU deployments and larger clusters; own multi-cloud storage and data replication.

Skills

Systems-level engineering

Strong coding ability

GPU infrastructure

K8s-first architecture

Cloud storage expertise

Tools

Kubernetes

NCCL

Job description

Reflection is building a Kubernetes-first, multi-cloud compute platform to enable scalable GPU workloads and fault-tolerant operations. You will help design and maintain tooling for automatic remediation, capacity planning, and performance debugging across large GPU fleets.

The role emphasizes integrating with training teams to co-design fault tolerance and remediation strategies, while aligning with a multi-cloud storage and data replication roadmap.