Staff Engineer, Distributed Storage and HPC & AI Infrastructure
Together AI17 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Architect and implement the storage technical strategy and roadmap while making high-performance architectural decisions for a growing GPU fleet.
- Engineer and scale multi-petabyte AI/ML storage systems using Vast, Weka, Ceph, and related technologies, including automated tiering and lifecycle policies.
- Design intelligent caching and tiered storage architectures for high IOPS and cluster-wide throughput across AI training and inference workloads.
- Tune L2/L3 storage isolation to provide secure, production-grade multi-tenancy.
- Build Kubernetes storage operators and controllers for automated provisioning, self-service abstractions, and quota enforcement.
- Optimize data paths, model-weight and dataset caching, parallel filesystems, and storage infrastructure across thousands of nodes.
- Perform benchmarking and profiling and contribute code to open-source storage projects and internal tooling.
Requirements
- 8+ years of storage engineering experience managing distributed storage at multi-petabyte scale.
- Proven experience deploying and operating high-performance storage for GPU/HPC clusters.
- Deep production experience with Kubernetes and cloud-native storage.
- Strong Go and Python programming skills for production systems, automation, operators, and tooling.
- BS/MS in Computer Science, Engineering, or equivalent practical experience.
- Technical leadership experience designing systems that materially improve performance, reliability, or cost efficiency, including 99.999%+ uptime.
- Deep expertise with Ceph, WekaFS, Lustre, Vast, GPFS, or similar parallel filesystems at multi-petabyte scale.
- Production object-storage experience with S3, MinIO, Ceph, or R2, including performance optimization and cost management.
- Experience with CSI drivers, StatefulSets, PersistentVolumes, storage operators, and custom controllers.
- Experience optimizing storage for GPU workloads, RDMA/InfiniBand networking, and parallel filesystem throughput.
- Experience with Terraform, Ansible, Helm, and ArgoCD/GitOps workflows.
- Advanced knowledge of ext4, XFS, LVM, NVMe optimization, and RAID configurations.
- Experience operating Prometheus, Grafana, and Thanos.
- Nice-to-have experience with GPU Direct Storage, NVMe-oF, storage networking, RDMA implementations, AI/ML storage patterns, and fio, iperf3, iostat, or blktrace.
About Together AI
Together AI builds an AI-native cloud platform for developers, offering high-performance inference, fine-tuning/model shaping, and large-scale pre-training on on-demand GPU clusters with APIs and managed services. It emphasizes open-source models that teams can run and adapt, and also provides infrastructure for decentralized and scalable workloads. Founded in 2022 and headquartered in San Francisco, it is privately held and reports notable customers including Cursor, ElevenLabs, Salesforce, and Zoom.