Member of Technical Staff (AI Infrastructure Engineer)

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.

Responsibilities

• 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.

Qualifications

• 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.

Required Skills

• 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.

Preferred Skills

• 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 TensorFlow or distributed training libraries.
• Background in HPC environments, parallel computing, and high-performance networking.
• Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices.
• Experience with container registries, image optimization, and multi-stage builds for ML workloads.

Required Experience

• 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, DevOps, 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.