Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

Design and deliver multi-petabyte storage systems for AI training and inference workloads. Architect high-performance parallel filesystems and object stores, evaluate cutting-edge technologies, and drive cost optimization. Build Kubernetes-native storage operators and self-service platforms for automated provisioning and multi-tenancy. Optimize end-to-end data paths, design multi-tier caching architectures, and implement intelligent prefetching.

Responsibilities

Design multi-petabyte AI/ML storage systems; integrate WekaFS, Ceph, etc.; lead capacity planning and cost optimization (30-50% savings via tiering, lifecycle policies, right-sizing).
Design/optimize RDMA, InfiniBand, 400GbE networks; tune for max throughput/min latency; implement NVMe-oF/iSCSI; troubleshoot bottlenecks; optimize TCP/IP for storage.
Build Kubernetes storage operators/controllers; enable automated provisioning, self-service abstractions, multi-tenant isolation, quotas; create reusable Helm/Terraform patterns.
Deliver 10-50 GB/s per GPU node; optimize caching (weights/datasets/checkpoints), parallel filesystems, and data paths; troubleshoot with profiling tools; scale to thousands of nodes.
Build multi-tier caches (local NVMe, distributed, object); optimize data locality and model-weight distribution; implement smart prefetching/eviction.
Implement monitoring, alerting, SLOs; design DR/backups with runbooks; run chaos engineering; ensure 99.9%+ uptime via proactive/automated remediation.
Partner with ML/SRE teams; mentor on storage best practices; contribute to open-source; write docs, postmortems, and public learnings.

Requirements

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
Deep expertise in WekaFS, Lustre, GPFS, BeeGFS, or similar parallel filesystems at multi-petabyte scale
Production experience with S3, MinIO, Ceph, or R2 including performance optimization and cost management
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)
Go and Python for automation, operators, and tooling
Terraform, Ansible, Helm, GitOps (ArgoCD)
Advanced knowledge of filesystems (ext4, xfs), LVM, NVMe optimization, RAID configurations
Prometheus, Grafana, Thanos architecture and operations

Skills

WekaFS
Ceph
Lustre
Go
Python
Kubernetes
RDMA
InfiniBand
400GbE
NVMe-oF
iSCSI
TCP/IP
Helm
Terraform
Prometheus
Grafana
Thanos
GPU Direct Storage (GDS)
100GbE
Storage snapshots
Cloning
Thin provisioning
Backup and disaster recovery
Storage encryption
Storage benchmarking
Profiling tools

Work Type

full-time
remote

Experience Level

8+ years in storage engineering
3+ years managing distributed storage at multi-petabyte scale

Education Level

BS/MS in Computer Science, Engineering, or equivalent practical experience

Salary/Compensations

$250,000 - $300,000

Benefits

startup equity
health insurance

About the Company

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.
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.
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.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.