Member of Technical Staff (AI Infrastructure Engineer)

Highlights

• Build and optimize large-scale AI training and inference clusters.
• Partner closely with Inference and Research teams to scale models.
• Keep critical training runs and inference services highly available.

Ideal candidate

Infrastructure-minded engineer with 3-5 years supporting ML systems who thrives on reliability and performance. You enjoy partnering with researchers and keeping large-scale training and inference running smoothly.

Requirements

• Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
• Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
• Experience with deploying and managing distributed training systems at scale
• Deep understanding of container orchestration and distributed systems architecture
• High level familiarity with LLM architecture and training processes
• Experience managing GPU clusters and optimizing compute resource utilization
• Python and C++ programming with focus on systems and infrastructure automation
• Hands-on experience with ML frameworks such as PyTorch in distributed training contexts
• Strong understanding of networking, storage, and compute resource management for ML workloads
• Experience developing APIs and managing distributed systems for both batch and real-time workloads
• Solid debugging and monitoring skills with expertise in observability tools
• Demonstrated experience managing large-scale Kubernetes deployments in production
• Proven track record with Slurm cluster administration and HPC workload management
• Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure
• Ideally, 3-5 years of relevant experience in ML systems deployment