---
title: 'Staff Engineer, Distributed Storage and HPC & AI Infrastructure at Together AI'
canonical: 'https://feeny.ai/job/staff-engineer-distributed-storage-and-hpc-ai-infrastructure-together-ai-qmwmf2cw8b8j'
type: 'job'
last_seen: '2026-09-11'
---

# Staff Engineer, Distributed Storage and HPC & AI Infrastructure at Together AI

- **Company:** Together AI
- **Location:** Bengaluru, India
- **Work type:** remote
- **Posted:** 2026-09-11
- **Last confirmed live:** 2026-09-11
- **Apply:** https://job-boards.greenhouse.io/togetherai/jobs/5226095007

## Job description

## About the Role

In this role, you will operate, scale, and optimize multi-petabyte storage systems purpose-built for the world’s largest AI training and inference workloads. You’ll manage and scale high-performance parallel filesystems and object stores, evaluate and integrate cutting-edge technologies such as Vast, Weka, Ceph, and Lustre, and solve the complex engineering challenges of operating at extreme throughput, low-latency data paths, and massive cluster-scale storage operations.

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.

## 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.

## Requirements

- 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

## Nice to Have Skills

- GPU Direct Storage (GDS), NVMe-oF, storage networking, RDMA implementations
- ML/AI storage patterns (model weights, checkpointing, dataset caching)
- Storage benchmarking and profiling tools (fio, iperf3, iostat, blktrace)

## About Together AI

Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.

## 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.

Please see our privacy policy at https://www.together.ai/privacy

## About Together AI

## Company Overview
- **One-liner**: Together AI is the AI Native Cloud, a full-stack platform for production AI that provides inference, fine-tuning, and GPU compute powered by cutting-edge systems research.
- **Entity Type**: Private (Series C)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2022
- **Founders**: Vipul Ved Prakash (CEO), Ce Zhang (CTO), Tri Dao (Chief Scientist), Chris Ré, Percy Liang

## Core Business
- **Primary industry**: AI Infrastructure / Cloud Computing
- **Target customers**: B2B – AI-native startups (e.g., Cursor, Decagon, Eleven Labs), enterprise SaaS (Salesforce, Zoom, Zomato), and AI researchers.
- **Mission**: “Significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models” — with a commitment to open and transparent AI development.

## Products & Services
- **Serverless Inference**: Fast on-demand inference for open-source models with no infrastructure management.
- **Batch Inference**: Asynchronous processing of massive workloads (up to 30B tokens per model) at reduced cost.
- **Dedicated Model Inference**: Deploy models on dedicated GPU infrastructure for speed and control.
- **Dedicated Container Inference**: GPU infrastructure optimized for generative media (video, audio, image).
- **Fine-Tuning**: Fine-tune open-source models using latest research techniques (improves accuracy, reduces hallucinations).
- **Accelerated Compute**: Self-serve instant clusters to thousands of GPUs, optimized with Together Kernel Collection.
- **Model Shaping**: Tools to accelerate inference, model shaping, and pre-training.
- **Managed Storage**: High-performance object storage and parallel filesystems with zero egress fees.
- **Sandbox**: Fast, secure code sandboxes for AI app/agent development environments.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed; recent Series C reportedly doubled valuation (amount undisclosed) [cbinsights.com](https://www.cbinsights.com/company/together-4)
- **Key Metric**: **Total Funding** – $1.334B (including $800M Series C announced in mid-2026) [cbinsights.com](https://www.cbinsights.com/company/together-4)
- **Notable Investors/Partners**: Kleiner Perkins, NVIDIA, Salesforce Ventures, Prosperity7 Ventures (Aramco), and 29+ others across rounds.
- **Growth Signals**: 
  - Headcount grew 91% YoY to ~280 employees [linkedin.com](https://www.linkedin.com/company/togethercomputer)
  - Acquired CodeSandbox (Dec 2024) and Refuel.AI (May 2025)
  - Customers include Cursor, Decagon, Eleven Labs, AI21, Hedra, Cartesia, Salesforce, Zoom, Zomato
  - Published nine papers at ICML 2026; open-sourced ParallelKernelBench

## Competitive Advantages
- **Full-stack AI platform** covering inference, fine-tuning, compute, storage, and sandboxes — all integrated.
- **World-class research team** co-designing algorithms, kernels, and hardware (e.g., Tri Dao, co-inventor of FlashAttention).
- **Open-source commitment** and contributions (models, datasets, research) build developer trust and ecosystem lock-in.
- **Cost performance**: Claims 2x faster inference and 60% lower cost vs. alternatives through research-driven optimizations.

## Strategic Focus
- Accelerate the shift to open-source AI by making it economically viable at scale.
- Continue investing in systems research (GPU programming, kernel optimization, model shaping) to improve performance and cost.
- Expand enterprise adoption through dedicated deployments and partnerships.
- Grow the platform’s capabilities (e.g., managed storage, sandboxes) to reduce friction for AI-native builders.

## Why Work Here
- **Culture**: Values include “Do more with less,” “Open and responsible development,” “Empower innovation,” and “Optimizers” — a research-driven, mission-oriented environment.
- **Remote/Hybrid/Office**: Primarily office-based in San Francisco (251 Rhode Island Street) with employees across 16 countries. Office perks include lunch, dinner, snacks, parking/transit stipends, and relocation support.
- **Benefits**: Competitive salary and equity, 401K matching, health/dental/vision insurance, flexible PTO, generous parental leave, life and disability protection.
- **Engineering Culture**: Deep focus on systems research and open-source; engineers work alongside leading AI researchers (e.g., Tri Dao, Ce Zhang). Talent sourced from Apple, Google, Amazon, NVIDIA, Stanford AI Lab.
- **Career Growth**: 53 active job postings (as of mid-2026); roles span ML engineering, platform engineering, research, and go-to-market. High growth trajectory offers rapid advancement.

## Sources
1. [together.ai/about-us](https://www.together.ai/about-us)
2. [together.ai/](https://www.together.ai/)
3. [together.ai/careers](https://www.together.ai/careers)
4. [linkedin.com/company/togethercomputer](https://www.linkedin.com/company/togethercomputer)
5. [cbinsights.com/company/together-4](https://www.cbinsights.com/company/together-4)

## Other roles at Together AI

- [Senior ABM & Campaign Manager](https://feeny.ai/job/senior-abm-campaign-manager-together-ai-san-francisco-b4m47ckm79bn) — San Francisco, CA
- [Technical Support Engineer - India](https://feeny.ai/job/technical-support-engineer-india-together-ai-pune-or-bangalore-mwn804cwswws) — Pune OR Bangalore, India
- [Senior Software Engineer — Infra Agent Systems](https://feeny.ai/job/senior-software-engineer-infra-agent-systems-together-ai-san-francisco-xc82f200r1ws) — San Francisco, CA
- [Senior ABM & Campaign Manager](https://feeny.ai/job/senior-abm-campaign-manager-together-ai-san-francisco-zc1zxaar2h1f) — San Francisco, CA
- [Senior Software Engineer — Infra Agent Systems](https://feeny.ai/job/senior-software-engineer-infra-agent-systems-together-ai-amsterdam-0kn67tn8tkvp) — Amsterdam, Netherlands
- [Senior Software Engineer — Infra Agent Systems UK](https://feeny.ai/job/senior-software-engineer-infra-agent-systems-uk-together-ai-london-bxf797ge7pxk) — London, United Kingdom
- [Senior Software Engineer — Infra Agent Systems Remote India](https://feeny.ai/job/senior-software-engineer-infra-agent-systems-remote-india-together-ai-india-4y1jwwt42nqs) — India
- [HR Coordinator- Amsterdam](https://feeny.ai/job/hr-coordinator-amsterdam-together-ai-amsterdam-e41wg9k2gakq) — Amsterdam, Netherlands
- [Staff Software Engineer, Inference / Compute Infrastructure Engineering](https://feeny.ai/job/staff-software-engineer-inference-compute-infrastructure-engineering-together-5j8djmf3gdbk) — London, United Kingdom
- [Junior/Senior or Staff Software Engineer, Inference / Compute Infrastructure Engineering](https://feeny.ai/job/junior-senior-or-staff-software-engineer-inference-compute-infrastructure-r7v2vs22snvc) — India