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Together AI

Staff Engineer, Distributed Storage and HPC & AI Infrastructure

Together AI · San Francisco

Posted about 3 months ago · $250,000 - $300,000

or apply directly on Together AI's site. We never take the application ourselves.

Is this posting real?

This role has been open
82 days
Together AI's roles stay open a median of 82 days
Reposted
No
Salary listed
Yes
72% of Together AI's roles list one
Ghost-job risk at Together AI
high
61 stale, 0 reposted of 61 open
Hiring momentum
77 roles opened in the last 90 days
↑ up vs. the prior 90 days
Last confirmed on the employer's board
2026-10-24

Measured from postings appearing on and disappearing from Together AI's own greenhouse board since 2026-08-03. Full hiring picture for Together AI.

About this role

In this role, you will operate and optimize large-scale storage systems designed for AI workloads, focusing on high-performance parallel filesystems and object stores. Your responsibilities include building caching and data-movement layers, developing Kubernetes-native storage operators, and ensuring efficient data paths for GPU nodes. You will also architect technical strategies for storage systems while integrating cutting-edge technologies and optimizing performance for training and inference workloads.

Our read on this posting3.8out of 5
benefits
3/5
freshness
1/5
career value
5/5
role clarity
5/5
pay transparency
5/5

Scored from the posting itself — how clearly the role is described, how much it says about pay and benefits, and how recently it was listed. Not a judgement of Together AI as an employer.

What you need

  • 8+ years in storage engineering, managing distributed storage at multi-petabyte scale
  • Proven track record building, deploying and operating high-performance storage for GPU/HPC clusters, for training and/or inference
  • Experience scaling storage for inference: KV-cache offload or disaggregated KV storage, prefix caching, model-weight distribution, or similar latency-critical data paths
  • 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

Nice to have

  • GPU Direct Storage (GDS), NVMe-oF, storage networking, RDMA implementations
  • GPUDirect RDMA and zero-copy data movement (ibverbs, NIXL, UCX), and KV-cache transfer frameworks (e.g. LMCache, Mooncake, Dynamo)
  • ML/AI storage patterns (model weights, checkpointing, dataset caching, KV-cache offload and prefix caching)
  • Storage benchmarking and profiling tools (fio, iperf3, iostat, blktrace)
  • Patents, publications (FAST, OSDI, SOSP, SC, ATC), or significant open-source contributions in storage or systems

What you get

  • competitive compensation
  • startup equity
  • health insurance
  • flexibility in terms of remote work
  • US base salary range of $250,000 - $330,000 + equity + benefits

Worth weighing

  • highly specialized role focused on storage systems for AI workloads
  • no mention of specific technologies beyond those listed as must-haves and nice-to-haves
  • the role requires extensive experience in a niche area of storage engineering

Summarised from Together AI's posting. Read the full original.

Listed by Together AI on their greenhouse job board, last confirmed open on 2026-10-24. PitchMeAI is not the employer.

More roles at Together AI

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