CircleCI Associate Rotation Program (New/Recent Grads)

CircleCI•San Francisco, CA

1d•Onsite

About The Position

The CircleCI Associate Rotation Program (CARP) is a 12-month program with two rotations designed for new and recent graduates to gain deep experience in engineering at a company focused on the intersection of AI and DevOps. Participants will work on real teams, own significant projects, and contribute to products used by engineers daily. The program emphasizes not only building software but also communicating and advocating for the future of AI in software development. This cohort is specifically for engineers who will rotate through core technical teams, including Platform Engineering, Product Engineering, and Data & ML Engineering, focusing on building and scaling AI-native features and infrastructure.

Requirements

Recent graduate ready to define software development in the AI era.
Thrives in ambiguity and proactively addresses unclear problems.
Moves fast, iterates quickly, and prioritizes action.
Has strong opinions but is open to discussion and can defend ideas.
Genuinely loves building things and wants to see work used in production.
Thinks of AI as a collaborator, not a threat.
Understands AI as a development paradigm, not just a productivity shortcut.
Uses AI tools natively in their workflow (prompting, iterating, shipping with AI assistance).
Understands the limitations of AI-generated code and its importance at scale.
Can reason about autonomous agents interacting with DevOps infrastructure.
Stays curious about advancements in the AI landscape.

Nice To Haves

Experience with AI-written code.
Experience in CI/CD.
Experience in DevOps.

Responsibilities

Rotate through core technical teams (Platform Engineering, Product Engineering, Data & ML Engineering).
Design and build infrastructure that validates AI-written code at machine speed.
Work on intelligent resource allocation that adapts dynamically to AI workload patterns.
Help achieve sub-second response times for systems.
Debug complex technical problems.
Build AI-powered features that predict build failures.
Create natural language interfaces for pipeline configuration.
Ship seamless integrations with AI coding assistants.
Turn vague product ideas into shipped code.
Build data pipelines processing billions of AI-generated build events in real time.
Develop ML models that identify patterns in AI code writing, testing, and breaking.
Create analytics products to help customers understand their AI-augmented dev velocity.
Communicate and advocate for the future of AI in software development among peers and customers.
Contribute to feature, data, or product communication and advocacy.