---
title: 'Research Engineer - RL Infrastructure at Prime Intellect'
canonical: 'https://feeny.ai/job/research-engineer-rl-infrastructure-prime-intellect-san-francisco-s38mrqzq4m59'
type: 'job'
last_seen: '2026-09-07'
---

# Research Engineer - RL Infrastructure at Prime Intellect

- **Company:** Prime Intellect
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2026-07-08
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/primeintellect/05e4b76b-2570-4c89-baf2-9833fff7378f

## 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.

We train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.

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.

## WHAT YOU’LL WORK ON

- Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/prime-rl framework.
- Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.
- Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.
- Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.
- Help shape the architecture of our RL training stack, including async rollout and post-training systems.
- Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.
- Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.
- Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.

YOU MAY BE A FIT IF YOU HAVE

- Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.
- Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.
- Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.
- Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.
- Strong understanding of GPU architecture, profiling, and performance debugging.
- Ability to identify bottlenecks across the stack and drive improvements from first principles.
- Comfort working in a fast-moving environment with ambiguous problems and high ownership.

## ESPECIALLY EXCITING

- Experience writing or optimizing CUDA / Triton kernels.
- Experience with compiler or runtime optimization for ML systems.
- Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.
- Experience with multi-node GPU clusters and high-performance networking.
- Contributions to open-source ML systems or infrastructure projects.
- Interest in publishing technical work or sharing insights through engineering blogs and technical writing.

## WHY THIS ROLE MATTERS

The next frontier in AI will not be unlocked by models alone. It will be unlocked by systems that let those models train faster, adapt continuously, and operate across real environments at scale.

That infrastructure does not exist yet in the form the world needs.

We’re building it.

## BENEFITS & PERKS

- Cash Compensation Range of $150-350k, plus equity.
- Flexible work arrangements, with the option to work remotely or in person from our San Francisco office.
- Visa sponsorship and relocation support for international candidates.
- Quarterly team offsites, hackathons, conferences, and learning opportunities.
- A deeply technical, high-agency team working on infrastructure for open superintelligence.

If you’re excited about building the systems foundation for frontier-scale RL and open superintelligence, we’d love to hear from you.

## 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

- [Head of Talent](https://feeny.ai/job/head-of-talent-prime-intellect-san-francisco-naf4r2rwx21x) — San Francisco, CA
- [Member of Technical Staff - Training Platform](https://feeny.ai/job/member-of-technical-staff-training-platform-prime-intellect-san-francisco-4btkkz3tw3ps) — San Francisco, CA
- [Member of Technical Staff - Sandbox Platform](https://feeny.ai/job/member-of-technical-staff-sandbox-platform-prime-intellect-san-francisco-qsr7247a01ar) — San Francisco, CA
- [Member of Technical Staff - Inference](https://feeny.ai/job/member-of-technical-staff-inference-prime-intellect-7syapkjxntgq)
- [Member of Technical Staff - GPU Infrastructure](https://feeny.ai/job/member-of-technical-staff-gpu-infrastructure-prime-intellect-san-francisco-yxe0m7p9mjqy) — San Francisco, CA
- [Member of Technical Staff - Full Stack Software Engineer](https://feeny.ai/job/member-of-technical-staff-full-stack-software-engineer-prime-intellect-san-enhkkcn3cfge) — San Francisco, CA
- [Member of Technical Staff - Compute Platform](https://feeny.ai/job/member-of-technical-staff-compute-platform-prime-intellect-san-francisco-00b0zmttc2p0) — San Francisco, CA
- [Research Engineer - Reinforcement Learning](https://feeny.ai/job/research-engineer-reinforcement-learning-prime-intellect-san-francisco-q3tyazz9enkn) — San Francisco, CA
- [Research Engineer - Distributed Training](https://feeny.ai/job/research-engineer-distributed-training-prime-intellect-san-francisco-6anh0v1a8nqx) — San Francisco, CA
- [Open Application for Unconventional Talent](https://feeny.ai/job/open-application-for-unconventional-talent-prime-intellect-san-francisco-g7rn6nfnxcg1) — San Francisco, CA