---
title: 'Member of Technical Staff - Storage Infrastructure at Prime Intellect'
canonical: 'https://feeny.ai/job/member-of-technical-staff-storage-infrastructure-prime-intellect-san-francisco-039n2ahf9xzy'
type: 'job'
last_seen: '2026-10-19'
---

# Member of Technical Staff - Storage Infrastructure at Prime Intellect

- **Company:** Prime Intellect
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-14
- **Last confirmed live:** 2026-10-19
- **Apply:** https://jobs.ashbyhq.com/primeintellect/ef5a79e9-4736-41a5-b608-c0b3856fb077

## Job description

Own Your Intelligence Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators, including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

## Role Impact

You'll build and operate the storage systems that feed frontier AI workloads. Own reliable, high-throughput access to datasets, checkpoints, and model artifacts, balancing performance, durability, availability, and cost as GPU clusters scale. Core Technical Responsibilities

- Design and operate storage architectures for training datasets, checkpointing, inference artifacts, and shared research workflows
- Deploy and tune parallel filesystems, object storage, and local NVMe caching for demanding AI workloads
- Benchmark throughput, latency, metadata performance, and concurrent access with representative training and checkpoint workloads
- Build provisioning, capacity planning, lifecycle management, and operational automation for storage services
- Design and test replication, recovery, backup, and failure-handling procedures with explicit durability and availability targets
- Diagnose performance and reliability issues across applications, clients, networks, filesystems, and devices
- Implement access controls, tenant separation, quotas, monitoring, and runbooks; collaborate with compute and networking teams

Technical Requirements Required Experience

- 3+ years building or operating production distributed storage systems
- Hands-on experience with at least one parallel or distributed filesystem or object storage platform, such as Lustre, BeeGFS, Ceph, or GPFS
- Strong Linux administration and performance troubleshooting skills
- Experience automating infrastructure operations in Python, Go, Bash, or similar languages
- Understanding of storage failure modes, data integrity, consistency, replication, and recovery

Infrastructure Skills

- Block, file, and object storage semantics and their performance tradeoffs
- NVMe/SSD performance, filesystem tuning, I/O profiling, and benchmarking
- High-throughput storage networking and distributed client behavior
- Capacity forecasting, observability, alerting, and safe maintenance procedures
- Authentication, authorization, encryption, and secure data lifecycle management

## Nice to Have

- Experience supporting large GPU training clusters and high-volume checkpoint workloads
- S3-compatible object storage, data tiering, or distributed caching
- RDMA-enabled storage or GPUDirect Storage experience
- Kubernetes storage integrations or SLURM environments
- Storage cost optimization and contributions to open-source storage systems

Growth Opportunity You'll work directly with customers pushing the boundaries of AI, from startups training foundation models to enterprises deploying massive inference infrastructure. You'll collaborate with our world-class engineering team while having direct impact on systems powering the next generation of AI breakthroughs. We value expertise and customer obsession - if you're passionate about building reliable, high-performance GPU infrastructure and have a track record of successful large-scale deployments, we want to talk to you. Apply now and join us in our mission to democratize access to planetary scale computing.

## Compensation

Cash compensation range of $150,000–$300,000 plus equity incentives.

## About Prime Intellect

## Company Overview
- **One-liner**: Prime Intellect provides an open, full-stack platform for companies to train, deploy, and continuously improve their own AI models through large-scale distributed reinforcement learning.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Vincent Weisser (CEO), Johannes Hagemann (CTO)

## Core Business
- **Primary industry**: AI infrastructure, open-source AI research, and agentic training platforms
- **Target customers**: B2B — enterprises and AI labs that want to train custom models without building their own infrastructure; also open-source community contributors
- **Mission**: Democratize frontier AI training by making it accessible to every company and collectively owning the resulting open innovations.

## Products & Services
- **Compute**: Access to GPU clusters (H100, H200, B200+) from 50+ providers, plus on-demand single GPU instances and reserved clusters with InfiniBand networking and SLURM/K8s orchestration.
- **Hosted Training (Lab)**: Managed large-scale reinforcement learning (RL) training without infrastructure overhead, including a 2,500+ environment hub and custom eval benchmarks.
- **Inference**: Serverless or dedicated inference for custom models, with native LoRA support and 1-click deployment.
- **Open-Source Libraries**: `prime-rl` (async RL framework), `verifiers` (environments & evals), and environment hub for collaborative development.
- **INTELLECT-3**: A 100B+ parameter Mixture-of-Experts model trained on their RL stack, achieving state-of-the-art performance for its size.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Total Funding**: $70.44M across multiple rounds (CBInsights, 2026); earlier reports indicated $20.5M in seed rounds, followed by a Series B round of $49.94M led by Founders Fund and other investors.
- **Notable Investors/Partners**: Founders Fund, CoinFund, Distributed Global, Radical Ventures, Andrej Karpathy, Clem Delangue (Hugging Face), Dylan Patel (SemiAnalysis), Tri Dao (Together AI).
- **Growth Signals**: Headcount grew 128.6% YoY to 36 employees; 24 active job postings (monthly job growth +26.3%); launched INTELLECT-3 and SYNTHETIC-2 datasets; customers include Ramp and Zap; operates in 9 countries.

## Competitive Advantages
- **Full-stack integrated platform** – compute, post-training, evals, and inference in one place, reducing the fragmentation of building and maintaining AI workflows.
- **Large-scale distributed RL expertise** – proven ability to train 100B+ models across clusters (global training via their RL stack).
- **Open-source ethos** – models, datasets, and libraries are released openly, attracting a community of researchers and developers.
- **Founding team & research edge** – strong ties to frontier AI research, with contributions like prime-rl and self-improving agent loops.

## Strategic Focus
- **Self-improving agents** – building infrastructure that closes the loop from deployment back to training, enabling models to compound performance over time.
- **Scaling RL at extreme sizes** – recent research (RL at 1T Scale) shows commitment to massive-scale reinforcement learning.
- **Enterprise adoption** – adding dedicated solutions engineers and managed workflows for corporate customers.

## Why Work Here
- **Culture**: Seeks “the most ambitious developers” and emphasizes cutting-edge research and engineering. The team includes former engineers from Together AI, Aleph Alpha, and Anyscale.
- **Remote/Hybrid**: Remote-friendly with HQ in San Francisco.
- **Growth trajectory**: Rapidly scaling company (headcount up 128% YoY) with multiple open roles across engineering, research, and go-to-market.
- **Tech stack**: Python, PyTorch, CUDA, Kubernetes, SLURM, Grafana, and a broad modern AI toolchain – opportunity to work on infrastructure-level challenges.

## Sources
1. [primeintellect.ai](https://www.primeintellect.ai/)
2. [linkedin.com/company/primeintellect-ai](https://www.linkedin.com/company/primeintellect-ai)
3. [jobs.ashbyhq.com/PrimeIntellect](https://jobs.ashbyhq.com/primeintellect)
4. [cbinsights.com/company/prime-intellect](https://www.cbinsights.com/company/prime-intellect)
5. [docs.primeintellect.ai/introduction](https://docs.primeintellect.ai/introduction)

## Other roles at Prime Intellect

- [Research Engineer - RL Infrastructure](https://feeny.ai/job/research-engineer-rl-infrastructure-prime-intellect-san-francisco-faaejgnzefb8) — San Francisco, CA
- [Research Engineer - Pretraining Data](https://feeny.ai/job/research-engineer-pretraining-data-prime-intellect-san-francisco-fbgk1zsg5rfx) — San Francisco, CA
- [Research Engineer - Data and Evaluation](https://feeny.ai/job/research-engineer-data-and-evaluation-prime-intellect-san-francisco-pbeyv35k8888) — San Francisco, CA
- [Research Engineer - Pretraining](https://feeny.ai/job/research-engineer-pretraining-prime-intellect-san-francisco-zemr10qw9rtd) — San Francisco, CA
- [Research Engineer - Inference](https://feeny.ai/job/research-engineer-inference-prime-intellect-san-francisco-1rywfz5aktgb) — San Francisco, CA
- [Research Engineer / Scientist - Reinforcement Learning](https://feeny.ai/job/research-engineer-scientist-reinforcement-learning-prime-intellect-san-francisco-14pw1t4w47jd) — San Francisco, CA
- [Member of Technical Staff - Datacenter Networking](https://feeny.ai/job/member-of-technical-staff-datacenter-networking-prime-intellect-san-francisco-nad0z4zmmdmc) — San Francisco, CA
- [Product Marketing Lead](https://feeny.ai/job/product-marketing-lead-prime-intellect-san-francisco-qmet74zfdsaq) — San Francisco, CA
- [Strategic Account Executive - AI Infrastructure](https://feeny.ai/job/strategic-account-executive-ai-infrastructure-prime-intellect-san-francisco-hv0skjf5w6zy) — San Francisco, CA
- [Founding Solutions Architect](https://feeny.ai/job/founding-solutions-architect-prime-intellect-san-francisco-rrwa9hfn3xgq) — San Francisco, CA