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Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. The Inference Ecosystem Engineering team’s mission is to show—not tell—the power of the Cerebras Inference API. We build open-source integrations, reference architectures, and polished demo apps that developers can clone, run, and extend in minutes. From LangChain agents to partner plug-ins and end-to-end “weekend projects,” our code is often the first (and most lasting) impression customers have of Cerebras.
Job Responsibility:
Design, develop, and maintain open-source libraries, SDKs, and sample repos that make Cerebras the easiest-to-adopt inference platform
Create production-quality demo applications that highlight low latency, high gen speed, and cost advantages
Build and own CI/CD pipelines, tests, and release automation for all public repos
Collaborate with partner engineering teams to embed Cerebras inference into their products and publish joint reference architectures
Collect developer feedback, identify usability gaps, and influence the Cerebras API roadmap
Contribute to engineering blogs, tutorials, and conference talks to grow community awareness and adoption
Requirements:
Bachelor’s or Master's degree in computer science or related field, or equivalent practical experience
4+ years professional software engineering experience (or equivalent open-source track record)
Solid understanding of GenAI applications and design patterns such as RAG
Proficiency in Python and/or TypeScript/JavaScript
Hands-on with at least one modern LLM framework (LangChain, LlamaIndex, CrewAI, AutoGen, etc.)
Multiple non-trivial open-source contributions, preferably to GenAI projects
Ability to move quickly from whiteboard idea to working prototype