Compute Intelligence Engineer

Prime Intellect•San Francisco, CA

6d•$225,000 - $300,000•Hybrid

About The Position

Prime Intellect is building the open superintelligence stack — from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full RL post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts. We recently raised $20M in funding, led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy, Tri Dao, Dylan Patel, Clem Delangue, Emad Mostaque, and many others. 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.

Requirements

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

Nice To Haves

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

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