About the company
CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025.
Responsibilities
- Contribute to the ongoing transition from monolithic to distributed architecture.
- Help design and implement architecture to be agentic ready, enabling rapid deployment of AI products and MCP server integrations.
- Make sound technical decisions for the components you own, balancing immediate needs with long-term goals.
- Write production-quality Python code for critical platform components.
- Build and optimize distributed compute services.
- Implement reliable workflow orchestration patterns.
- Contribute to CI/CD pipelines, automated testing, and deployment for platform services.
- Support reliability, observability, and SRE practices (monitoring, alerting, incident response, performance and scaling).
- Support and mentor engineers, sharing knowledge across the team.
- Follow and help improve engineering best practices and patterns.
- Collaborate with product and engineering peers to turn requirements into technical solutions.
- Contribute to technical decision-making and prioritization within your team.
- Interview and onboard new engineers.
Requirements
- 6+ years of software engineering experience.
- Solid software engineering breadth across deployment and CI/CD, automated testing, and production reliability (observability, incident response).
- Experience with distributed systems, building and operating backend services end-to-end.
- Experience designing APIs and service interfaces.
- Security-minded development, including authentication/authorization (e.g., OAuth) and secure-by-default practices.
- Strong Python development skills with production experience.
- Experience with Kubernetes in production.
- Familiarity with workflow orchestration tools (Temporal, Airflow, Kubeflow, or similar).
- Cloud platform experience (AWS preferred, Azure & GCP a plus).
- Familiarity with data processing frameworks (Spark, Athena, Ray, or similar) a plus.
- Strong technical communication and collaboration skills.