Prime Intellect
Compute Intelligence Engineer
About this role
As a Compute Intelligence Engineer at Prime Intellect, you will design and implement the data infrastructure that supports the company's compute operations. Your work will enhance visibility into compute resources, enabling effective management of supply and demand across various teams, thereby powering critical business decisions.
What you'll do
- Establish and maintain the data warehouse and pipelines for compute telemetry and billing
- Develop data models and transformations to ensure clean, trustworthy data
- Create dashboards for real-time insights on compute supply and demand
- Build a queryable layer for AI access to streamline information retrieval for teams
- Track compute supply end-to-end and identify utilization bottlenecks
- Ensure operational reliability of data systems and promote data quality standards
What they're looking for
- Data engineering and analytics experience
- Proficient in building and maintaining data warehouses
- Strong knowledge of Python and SQL for pipeline creation
- Experience with transformations using dbt or similar tools
- Familiarity with Snowflake, BigQuery, or Databricks
- Skill in connecting systems via APIs
- Ability to create dashboards and reports
- Problem-solving and cross-functional collaboration
Benefits
- Competitive salary and equity opportunities
- Supportive work environment for innovation
- Access to advanced technology and tools
- Collaborative team culture
- Opportunity to shape the company’s data infrastructure
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Prime Intellect
Prime Intellect builds AI infrastructure and training systems, with a focus on reinforcement learning, distributed training, and compute optimization. The company is hiring Research Engineers and Infrastructure Engineers to develop synthetic data pipelines, decentralized training infrastructure, compute visibility systems, and large-scale RL training optimization.
View all jobs at Prime IntellectLikely interview questions
- Walk us through a time you built a data warehouse or central data platform from scratch. What were the biggest challenges in getting different data sources to sync reliably, and how did you solve them?
- Describe your experience modeling data with dbt or similar tools. How do you think about designing schemas and transformations that stay maintainable as business requirements change?