Compute Intelligence Engineer

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.

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

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 have

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

Benefits

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; Meaningful equity