Member of Technical Staff; AI Infrastructure Engineer

Overview

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters.

• Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads
• Manage and optimize Slurm-based HPC environments for distributed training of large language models
• Develop robust APIs and orchestration systems for both training pipelines and inference services
• Implement resource scheduling and job management systems across heterogeneous compute environments
• Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
• Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
• Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
• Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands

• 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 (Multi‑Head Attention, Multi/Grouped‑Query, distributed training strategies)
• Experience managing GPU clusters and optimizing compute resource utilization

• Expert‑level Kubernetes administration and YAML configuration management
• Proficiency with Slurm job scheduling, resource management, and cluster configuration
• 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 for containerized environments

• Experience with Kubernetes operators and custom controllers for ML workloads
• Advanced Slurm administration including multi‑cluster federation and advanced scheduling policies
• Familiarity with GPU cluster management and CUDA optimization
• Experience with other ML frameworks like Tensor Flow or distributed training libraries
• Background in HPC environments, parallel computing, and high‑performance networking
• Knowledge of infrastructure as code (Terraform, Ansible) and Git Ops practices
• Experience with container registries, image optimization, and multi‑stage builds for ML workloads

• Demonstrated experience managing large‑scale Kubernetes deployments in production environments
• Proven track record with Slurm cluster administration and HPC workload management
• Previous roles in SRE, Dev Ops, or Platform Engineering with focus on ML infrastructure
• Experience supporting both long‑running training jobs and high‑availability inference services
• Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management