AI Infrastructure Engineer; Member of Technical Staff

• 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

High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)

Experience with deploying and managing distributed training systems at scale

Deep understanding of container orchestration and distributed systems architecture

Experience managing GPU clusters and optimizing compute resource utilization

Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization

Expert-level Kubernetes administration and YAML configuration management

Python and C++ programming with focus on systems and infrastructure automation

Proficiency with Slurm job scheduling, resource management, and cluster configuration

Experience developing APIs and managing distributed systems for both batch and real-time workloads

Strong understanding of networking, storage, and compute resource management for ML workloads

Hands-on experience with ML frameworks such as PyTorch in distributed training contexts

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

Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Experience supporting both long-running training jobs and high-availability inference services

Proven track record with Slurm cluster administration and HPC workload management

Demonstrated experience managing large-scale Kubernetes deployments in production environments

Previous roles in SRE, Dev Ops, or Platform Engineering with focus on ML infrastructure