Compute Intelligence Engineer

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.

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.

Responsibility

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

Team off-sites and conferences

A front-row seat to building the infrastructure layer for open AI