AI Engineer

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 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.

Skills and Qualifications

• A strong portfolio of AI applications, prototypes, technical writing, tutorials, or other substantive developer-facing work.

• Hands‑on experience building with model APIs and debugging AI application workflows.

• Excellent technical writing skills. You can turn a complex implementation or experimental result into a clear, useful explanation.

• Experience working with agent harnesses or related workflows involving prompting, tool calling, context management, and evaluation.

• An informed view of the AI developer ecosystem and curiosity about new models, frameworks, and emerging application patterns.

• Strong technical judgment. You can design fair comparisons, verify results, and distinguish observations from assumptions.

• Original ideas for demos and developer experiences, with a clear sense of what makes an example useful and engaging.

• The ability to scope projects, ship independently, incorporate feedback, and collaborate with technical and creative partners.

Preferred Experience

• Experience optimizing latency, responsiveness, or reliability in AI applications.

• Contributions to open‑source projects, developer tools, or publicly available technical examples.

• Experience teaching developers through workshops, talks, or live demonstrations.

Portfolio

Please include links to code, prototypes, and technical writing you’ve created. Identify what you personally built or wrote, explain the problem you explored, and share the results or lessons learned.

Cerebras Systems is an equal opportunity employer. We welcome different backgrounds and perspectives and strive to create an inclusive environment where people can learn, grow, and do their best work.

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

1. Build a breakthrough AI platform beyond the constraints of the GPU.

2. Publish and open source their cutting‑edge AI research.

3. Work on one of the fastest AI supercomputers in the world.

4. Enjoy job stability with startup vitality.

5. Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here !