Staff Engineer, Distributed Storage and HPC & AI Infrastructure

Role Description

In this role, you will design and deliver multi-petabyte storage systems purpose-built for the world’s largest AI training and inference workloads. You’ll architect high-performance parallel filesystems and object stores, evaluate and integrate cutting-edge technologies such as WekaFS, Ceph, and Lustre, and drive aggressive cost optimization—routinely achieving 30-50% savings through intelligent tiering, lifecycle policies, capacity forecasting, and right-sizing.

You will also build Kubernetes-native storage operators and self-service platforms that provide automated provisioning, strict multi-tenancy, performance isolation, and quota enforcement at cluster scale. Day-to-day, you’ll optimize end-to-end data paths for 10-50 GB/s per node, design multi-tier caching architectures, implement intelligent prefetching and model-weight distribution, and tune parallel filesystems for AI workloads.

Qualifications

• 8+ years in storage engineering with 3+ years managing distributed storage at multi-petabyte scale
• Proven track record deploying and operating high-performance storage for GPU/HPC clusters
• Deep Kubernetes and cloud-native storage experience in production environments
• Strong coding skills in Go and Python with demonstrated ability to build production-grade tools
• BS/MS in Computer Science, Engineering, or equivalent practical experience
• History of technical leadership: designing systems that significantly improved performance (>3x), reliability (99.9%+ uptime), or cost efficiency
• Distributed Storage Systems: Deep expertise in WekaFS, Lustre, GPFS, BeeGFS, or similar parallel filesystems at multi-petabyte scale
• Object Storage: Production experience with S3, MinIO, Ceph, or R2 including performance optimization and cost management
• Kubernetes Storage: CSI drivers, StatefulSets, PersistentVolumes, storage operators, and custom controllers
• Storage optimization for GPU workloads, RDMA/InfiniBand networking, parallel filesystem optimization (100+ GB/s aggregate cluster throughput)
• Programming: Go and Python for automation, operators, and tooling
• Infrastructure as Code: Terraform, Ansible, Helm, GitOps (ArgoCD)
• Linux Storage Stack: Advanced knowledge of filesystems (ext4, xfs), LVM, NVMe optimization, RAID configurations
• Observability: Prometheus, Grafana, Thanos architecture and operations

Requirements

• GPU Direct Storage (GDS), NVMe-oF, storage networking (100GbE/400GbE)
• ML/AI storage patterns (model weights, checkpointing, dataset caching)
• Kubernetes operator development (controller-runtime, kubebuilder)
• Storage snapshots, cloning, and thin provisioning
• Backup and disaster recovery (Velero, Restic, cross-region replication)
• Storage encryption (at-rest and in-transit), security and compliance
• Storage benchmarking and profiling tools (fio, iperf3, iostat, blktrace)

Benefits

• Competitive compensation
• Startup equity
• Health insurance
• Flexibility in terms of remote work

Company Description

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.