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 comput