---
title: 'Compute Intelligence Engineer at Prime Intellect'
canonical: 'https://feeny.ai/job/compute-intelligence-engineer-prime-intellect-san-francisco-kzrsn7x7d0s2'
type: 'job'
last_seen: '2026-11-09'
---

# Compute Intelligence Engineer at Prime Intellect

- **Company:** Prime Intellect
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2026-07-08
- **Last confirmed live:** 2026-11-09
- **Apply:** https://jobs.ashbyhq.com/PrimeIntellect/5366fc55-df00-4c2c-b389-8850a9393840

## 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.

## Your Role

Compute is the foundational input of everything Prime Intellect does — and right now, the picture of our compute supply, demand, and economics lives across spreadsheets, partner conversations, and people's heads. This role changes that. As Compute Intelligence Engineer, you'll build the data infrastructure and intelligence platform that gives the entire company a live, accurate picture of our compute: what we have, what's coming online, where our bottlenecks are, and how supply maps to demand. This is a hands-on data engineering build — you'll stand up the warehouse, write the pipelines that pull from our compute telemetry, billing systems, partner data, and CRM, model that data into a clean and trustworthy source of truth, and turn it into dashboards and a queryable layer the whole company relies on. This is a builder-first role with a clear business purpose. You won't be building data infrastructure for its own sake — you'll be building the system that lets our Compute Partnerships team, Growth team, and Research team operate from the same source of truth. When Growth needs to know what capacity is coming online next quarter, when Compute Partnerships needs to understand our utilization against commitments, when Research needs to scale a training run — the platform you build is what they'll turn to. You'll be early in this seat, and the foundations you lay will be the data backbone the company scales on.

## Responsibilities

Build the Compute Intelligence Platform

- Stand up Prime Intellect's data warehouse (Snowflake, BigQuery, or equivalent) and the pipelines that feed it — compute telemetry, billing and usage data, partner and supply data, CRM, and financial systems
- Build the data models and transformations (dbt or equivalent) that turn raw data into a clean, queryable, trustworthy source of truth
- Build dashboards and reporting that give the company a live picture of compute supply, demand, utilization, upcoming capacity, and bottlenecks
- Build a queryable, AI-accessible layer on top of the warehouse so teams across the company can answer their own questions without going through a data analyst

Supply & Demand Intelligence

- Build the data systems that track our compute supply end-to-end: what we have, what's committed, what's coming online, and what's utilized vs. idle
- Develop the views and models that surface where our bottlenecks are — and make upcoming supply legible to the teams that depend on it
- Connect supply data to demand signals so the company can see, in one place, how capacity maps to what we're selling and building

Cross-Functional Enablement

- Serve as the data backbone connecting Compute Partnerships, Growth, and Research — building the systems that let them operate from shared, accurate information
- Partner with Growth on understanding upcoming supply and how it maps to what they can sell
- Partner with Compute Partnerships on utilization, commitments, and supply tracking
- Partner with Research on scaling needs and capacity planning

Operational Reliability

- Build pipelines and systems that run unattended, stay in sync, and fail gracefully
- Establish the data quality, documentation, and infrastructure standards that let the data layer scale with the company
- Partner with Engineering on shared infrastructure, security, and data standards

## What We're Looking For

- 3–7+ years in data engineering, analytics engineering, GTM/growth engineering, or similar roles where you've built data infrastructure that served real business outcomes
- Strong technical skills: comfortable building and maintaining data warehouses, writing production-quality pipelines (Python, SQL), modeling data (dbt or equivalent), and connecting disparate systems via APIs
- Experience with modern data stack tooling — Snowflake / BigQuery / Databricks, dbt, orchestration (Airflow, Dagster, etc.), and BI/dashboarding tools
- A builder's instinct paired with business judgment — you don't just build what's asked; you understand the business well enough to build the right thing
- Comfortable being the data backbone for cross-functional teams — translating between business needs and the systems that serve them
- Familiarity with modern AI tooling and an interest in building AI-accessible data layers (natural-language querying, LLM-powered analytics) that let non-technical teams self-serve
- High ownership — you see gaps and build the fix before anyone asks
- Comfortable in ambiguity and speed; you'll be defining what the data layer looks like from scratch
- AI-native in how you work: you use LLMs, automation, and programmatic tools to move faster

Bonus:

- Experience as an early data hire who built a company's data infrastructure from scratch
- Familiarity with GPU economics, compute infrastructure, cloud telemetry, or AI/ML workloads
- Background in GTM engineering, growth engineering, or revenue/operations data
- Experience building LLM-powered or natural-language data interfaces
- Working knowledge of usage-based / consumption-based business models and the data they generate

## What We Offer

- Cash Compensation Range of $225-300k + meaningful equity
- Flexible work (remote or San Francisco)
- Visa sponsorship and relocation support
- Professional development budget
- Team off-sites and conferences
- A front-row seat to building the infrastructure layer for open AI

## 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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- [Office Manager](https://feeny.ai/job/office-manager-prime-intellect-san-francisco-8hvqgjz420hh) — San Francisco, CA
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