---
title: 'Member of Technical Staff - Training Platform at Prime Intellect'
canonical: 'https://feeny.ai/job/member-of-technical-staff-training-platform-prime-intellect-san-francisco-4btkkz3tw3ps'
type: 'job'
last_seen: '2026-09-14'
---

# Member of Technical Staff - Training Platform at Prime Intellect

- **Company:** Prime Intellect
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-08
- **Last confirmed live:** 2026-09-14
- **Apply:** https://jobs.ashbyhq.com/primeintellect/8706578d-5a01-4270-9d43-ed9cd998a982

## 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 help build our hosted training platform - the product that lets users launch LoRA and full fine-tuning runs on managed GPU clusters with a single API call or a few clicks. The role spans the developer-facing platform and the underlying Kubernetes-based training infrastructure that runs the jobs.

Core Technical Responsibilities

Hosted Training Infrastructure

- Design and operate Kubernetes-based training and inference orchestration across multi-cluster, multi-cloud GPU fleets
- Build and maintain Helm charts that compose trainers, inference servers, environment servers, and supporting services into reproducible "Training stacks"
- Develop the Python control-plane agents that watch pods, report run state to the platform, and keep clusters in sync
- Implement scheduling and autoscaling for heterogeneous hardware (H100/H200/B200) using KEDA, LeaderWorkerSet, taints/tolerations, and gang scheduling
- Run a tight GitOps workflow - every change ships through PRs, Helm values, and CI
- Build node-local model caches, checkpoint pipelines, and shared storage for fast cold starts
- Operate the observability stack (Prometheus, Grafana, Loki, DCGM) and make GPU cluster debugging fast

Platform Development

- Build the developer-facing surfaces for hosted training: job submission, live run monitoring, logs, metrics, model/adapter management, comparisons
- Develop FastAPI backend services and REST APIs that bridge the platform to running clusters
- Build real-time monitoring and debugging tools (streaming logs, step-level metrics, failure analysis)
- Ship product UI in Next.js / React / TypeScript with shadcn, Tailwind, tRPC, and TanStack Query

Research Bridge

- Interface with the RL trainer, inference servers, and environment servers running inside our clusters
- Productize new training capabilities (new model architectures, RL algorithms, modes)

Technical Requirements We're looking for engineers who are fluent across three areas - you don't need to be the world's best at any one, but you should have real depth in all three and a clear point of view on how they connect.

AI & GPU Landscape

- Strong working knowledge of the modern AI stack - open model families, finetuning techniques (LoRA, QLoRA, full FT, RLHF/RLAIF), inference engines (vLLM, SGLang, TensorRT-LLM)
- Familiarity with GPU hardware tradeoffs (H100 / H200 / B200, NVLink, interconnects, memory hierarchy) and what they mean for training and inference workloads
- Understanding of distributed training fundamentals (data/tensor/pipeline/expert parallelism, NCCL, multi-node scheduling)
- Awareness of what's happening at the frontier - new models, training methods, infra patterns - and the ability to translate that into product decisions

Kubernetes & Infrastructure

- Strong Kubernetes operations experience - Helm, CRDs, operators, KEDA, gang scheduling, GPU operator
- Comfortable debugging real production clusters (kubectl, pod lifecycle, node issues, networking)
- Cloud platform experience (GCP preferred - GCS, GKE, Cloud Run, Cloud Tasks)
- Infrastructure automation (Helm, Terraform, Ansible) and a GitOps mindset
- Observability: Prometheus, Grafana, Loki, OpenTelemetry, DCGM
- Linux fundamentals: networking, namespaces, performance tuning

Programming & Platform

- Strong Python backend development (FastAPI, async, SQLAlchemy)
- Comfortable building Python control-plane agents that talk to Kubernetes APIs
- Modern frontend development (TypeScript, React/Next.js, Tailwind, shadcn) - enough to ship product surfaces end-to-end
- REST and tRPC API design
- Experience building developer tools, dashboards, and live-monitoring UIs

## What We Offer

- Cash compensation $150K–$300K with significant equity
- Flexible work arrangement (remote or San Francisco office)
- Full visa sponsorship and relocation support
- Professional development budget for courses and conferences
- Regular team off-sites and conference attendance
- Opportunity to shape the future of decentralized AI development

Growth Opportunity You'll join a team of experienced engineers and researchers working on cutting-edge problems in AI infrastructure. We believe in open development and encourage team members to contribute to the broader AI community through research and open-source work. We value potential over perfection - if you're passionate about democratizing AI development and have experience in either platform or infrastructure development (ideally both), we want to talk to you.

Ready to help shape the future of AI? Apply now and join us in our mission to make powerful AI models accessible to everyone.

## 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)

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- [Member of Technical Staff - Sandbox Platform](https://feeny.ai/job/member-of-technical-staff-sandbox-platform-prime-intellect-san-francisco-qsr7247a01ar) — San Francisco, CA
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