AI Engineer

About the role

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 looking for an AI Engineer who loves building with new models, experimenting with developer tools, and writing about what they learn. You’ll create prototypes that demonstrate what fast inference makes possible, optimize the harnesses around models, and turn your findings into technical content that helps other developers build.

This is a hands-on role on our DevRel team. Technical writing is a core part of the job. You’ll take projects from an interesting question through implementation, testing, and a clear explanation others can follow. You should be as comfortable debugging an agent workflow or comparing model behavior as you are writing a tutorial or presenting a demo.

WHAT YOU’LL OWN

- Technical writing: Write blogs, tutorials, walkthroughs, and practical guides grounded in your own experiments. Explain how things work, why implementation choices matter, and how developers can reproduce your results.

- Prototypes and demos: Find original ways to showcase Cerebras fast inference. Build sample applications, notebooks, and interactive experiences that make the benefit tangible.

- Harness optimization: Build and improve the code and workflows around models, including prompts, tool use, context management, orchestration, and evaluation. Identify bottlenecks and test ways to improve speed, reliability, and output quality.

- Model experimentation: Try new models and investigate their capabilities, limitations, and fit for different tasks. Compare behavior on realistic workloads and turn useful findings into examples and recommendations for developers.

- Evaluation and reproducibility: Design practical tests, measure results, and validate technical claims. Make your code easy to run and explain the assumptions, tradeoffs, and limitations behind your conclusions.

- Launches and developer education: Partner with Product, Engineering, and New Media to create technically accurate launch examples, workshops, and stories. Present your work and help developers understand how to apply it.

- Developer feedback: Engage with developers, troubleshoot integrations, and turn recurring questions and friction into improvements to examples, documentation, and the product experience.