Senior Data Platform Engineer

Core Weave, the AI Hyperscaler™ acquired Weights & Biases to create the most powerful end-to-end platform to develop, deploy, and iterate AI faster. Since 2017, Core Weave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME
100 most influential companies of 2024. By bringing together Core Weave’s industry-leading cloud infrastructure with the best-in-class tools AI practitioners know and love from Weights & Biases, we’re setting a new standard for how AI is built, trained, and scaled.

The integration of our teams and technologies is accelerating our shared mission: to empower developers with the tools and infrastructure they need to push the boundaries of what AI can do. From experiment tracking and model optimization to high-performance training clusters, agent building, and inference at scale, we’re combining forces to serve the full AI lifecycle — all in one seamless platform.

Weights & Biases has long been trusted by over 1,500 organizations — including AstraZeneca, Canva, Cohere, OpenAI, Meta, Snowflake, Square,Toyota, and Wayve — to build better models, AI agents and applications. Now, as part of Core Weave, that impact is amplified across a broader ecosystem of AI innovators, researchers, and enterprises.

As we unite under one vision, we’re looking for bold thinkers and agile builders who are excited to shape the future of AI alongside us. If you're passionate about solving complex problems at the intersection of software, hardware, and AI, there's never been a more exciting time to join our team.

• Design and implement scalable data systems and pipelines — 30%:
  Architect, develop, test, and maintain distributed batch and streaming pipelines that ingest, transform, validate, and deliver structured and semi-structured data across production systems.
• Develop data transformation and orchestration frameworks — 20%:
  Build and optimize workflows using technologies such as dbt, Dagster, Airflow, or comparable tools. Establish testing, deployment, dependency-management, data-quality, and observability standards for these workflows.
• Improve data warehouse performance and reliability — 15%:
  Design data models, schemas, partitioning strategies, and storage patterns for large-scale analytical workloads. Diagnose and resolve performance, scalability, availability, and cost-efficiency issues within multi-petabyte data environments.
• Build product telemetry and data instrumentation — 15%:
  Partner with software engineering teams to define event schemas and implement reliable logging, telemetry, and instrumentation for web applications, backend services, and distributed systems.
• Develop data catalog and semantic-layer capabilities — 10%:
  Design metadata, lineage, governance, and semantic-modeling systems that enable consistent business definitions and secure self-service analytics.
• Support data-powered AI applications — 5%:
  Establish data interfaces and platform foundations for internal AI applications, including systems that support automated data discovery, workflow assistance, customer-growth analysis, churn-risk modeling, and abuse detection.
• Operate and support the platform — 5%:
  Monitor production systems, troubleshoot incidents, document operational procedures, and participate in an on-call rotation after completing the required onboarding and training.

This position requires independent technical judgment to evaluate architectural alternatives, identify system constraints, and select appropriate data structures, algorithms, database designs, and distributed-processing patterns.

• Bachelor’s degree or foreign equivalent in Computer Science, Computer Engineering, Data…