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

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 (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; 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

Nice to have

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