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About the Role

Prime Intellect is building the open superintelligence stack, enabling anyone to create, train, and deploy frontier agentic models. This role involves building the data infrastructure and intelligence platform to provide a live, accurate picture of the company's compute resources, supply, demand, and economics.

Responsibilities

• Build the Compute Intelligence Platform, including data warehouse and pipelines for compute telemetry, billing, partner data, and CRM.
• Develop data models and transformations using dbt or equivalent to create a trustworthy source of truth.
• Create dashboards and reports for live insights into compute supply, demand, utilization, and bottlenecks.
• Build an AI-accessible query layer for self-service data analysis across the company.
• Develop systems to track compute supply end-to-end, including committed and upcoming capacity.
• Surface bottlenecks and make upcoming supply visible to dependent teams.
• Connect supply data with demand signals to map capacity to sales and development efforts.
• Serve as the data backbone connecting Compute Partnerships, Growth, and Research teams.
• Partner with Growth on upcoming supply and sales mapping.
• Partner with Compute Partnerships on utilization, commitments, and supply tracking.
• Partner with Research on scaling needs and capacity planning.
• Build reliable, unattended pipelines and systems with graceful failure mechanisms.
• Establish data quality, documentation, and infrastructure standards for scalability.
• 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 building data infrastructure for business outcomes.
• Proficiency in building and maintaining data warehouses.
• Experience writing production-quality pipelines using Python and SQL.
• Experience modeling data with dbt or equivalent.
• Experience connecting disparate systems via APIs.
• Familiarity with modern data stack tooling: Snowflake/BigQuery/Databricks, dbt, orchestration tools (Airflow, Dagster, etc.), and BI/dashboarding tools.
• A builder's instinct paired with business judgment.
• Comfortable serving as the data backbone for cross-functional teams.
• Familiarity with modern AI tooling and an interest in building AI-accessible data layers.
• High ownership and ability to identify and fix gaps proactively.
• Comfortable operating in ambiguity and at speed.
• AI-native work approach, utilizing LLMs, automation, and programmatic tools.
• Experience as an early data hire building company data infrastructure from scratch (Bonus).
• Familiarity with GPU economics, compute infrastructure, cloud telemetry, or AI/ML workloads (Bonus).
• Background in GTM engineering, growth engineering, or revenue/operations data (Bonus).
• Experience building LLM-powered or natural-language data interfaces (Bonus).
• Working knowledge of usage-based/consumption-based business models and their data (Bonus).

Skills

• Data Engineering
• Analytics Engineering
• GTM/Growth Engineering
• Data Warehousing
• Python
• SQL
• dbt
• API Integration
• Snowflake
• BigQuery
• Databricks
• Airflow
• Dagster
• BI/Dashboarding Tools
• AI Tooling
• LLMs
• Automation
• Programmatic Tools
• GPU Economics
• Compute Infrastructure
• Cloud Telemetry
• AI/ML Workloads
• Usage-based Business Models

Location

• Remote
• San Francisco

Work Type

• Remote
• Onsite

Experience Level

• 3-7+ years

Salary/Compensations

• $225-300k

Benefits

• Meaningful equity
• Flexible work
• Visa sponsorship
• Relocation support
• Professional development budget
• Team off-sites and conferences

About the Company

• 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.
• This is a front-row seat to building the infrastructure layer for open AI.