Full Stack Engineer - Console Team

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About The Role

We’re hiring a full-stack engineer to build the critical parts of the Cerebras Developer Console — the primary interface developers and enterprises use to run and manage inference workloads.

This is a deeply technical, end-to-end role. You’ll build high-quality frontend systems (Next.js, TypeScript) and design backend services (GraphQL, Postgres, Redis) that power usage tracking, billing, quotas, and observability. The systems you build will operate at high scale, require careful data modeling, and balance real-time and batch processing. You’ll be expected to make strong architectural decisions and move quickly from idea to production.

You’ll join an existing, high-velocity team and take ownership of major platform areas such as billing, request logs, and metrics. The work directly impacts customer experience and revenue, and the expectations are correspondingly high.

We’re looking for someone who thrives in fast-moving environments, operates with urgency, and is comfortable navigating ambiguity while shipping high-quality systems.

What You’ll Do

• Build and evolve core systems — design and implement APIs, services, and UI that power the Developer Console and scale with growing customer usage.

• Make architectural decisions — define system boundaries, data models, and tradeoffs across real-time vs batch processing, performance, and cost.