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Staff Engineer, Distributed Storage and HPC & AI Infrastructure

Together AI· Engineering
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San Francisco$250k - $300kfull-timePosted 4mo ago

About the role

Leader for building and optimizing multi-petabyte storage systems and Kubernetes-native storage platforms for AI training and inference at scale, with emphasis on high-throughput data paths, multi-tenant storage, and integration of advanced storage technologies.

Responsibilities

  • Architect and implement the technical strategy and storage roadmap for Together AI, driving high-performance architectural decisions as we scale our GPU fleet.
  • Engineer and scale multi-petabyte AI/ML storage systems by integrating Vast, Weka, and Ceph while executing deep cost optimization through automated tiering and lifecycle policies.
  • Develop intelligent caching and tiered storage architectures to achieve extreme IOPS and cluster-wide throughput at GPU scale for training and inference workloads.
  • Tune storage isolation at the L2/L3 network layers to ensure secure, production-grade multi-tenancy for storage clients.
  • Code Kubernetes storage operators and controllers to enable automated provisioning, self-service abstractions, and quota enforcement.
  • Engineer end-to-end data paths to achieve 10+ GB/s per GPU node; architect multi-tier caching for model weights and datasets; tune parallel filesystems using advanced profiling; and scale storage infrastructure across thousands of nodes.
  • Optimize end-to-end data paths through advanced benchmarking and profiling, contributing high-impact code to open-source storage projects and internal tooling.

Qualifications

  • 8+ years in storage engineering, 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 systems and tooling
  • BS/MS in Computer Science, Engineering, or equivalent practical experience
  • History of technical leadership: designing systems that significantly improved performance, reliability (99.999%+ uptime), or cost efficiency
  • Distributed Storage Systems: Deep expertise in either of Ceph, WekaFS, Lustre, Vast, GPFS, 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 (TB/s aggregate cluster throughput - line saturation)
  • 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

Skills

Required:GoPython

Benefits

  • competitive compensation
  • startup equity
  • health insurance
  • flexibility in terms of remote work
Last updated Sep 9, 2026. Interested? Apply on the company site.

About Together AI

The AI Acceleration Cloud - Fast Inference, Fine-Tuning & Training

Run and fine-tune generative AI models with simple APIs and scalable GPU clusters. Train & deploy at scale on The AI Acceleration Cloud.

AI native cloudFine-TuningGPU clustersInferenceTrainingai-infrastructure-and-developmentbrex-fastest-growing-2025liquid-ai-recruiting-target
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