Compute Intelligence Engineer

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