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.
Define the DevRel team vision, roadmap, and quality bar in support of Cerebras’ developer and business goals.
Own demos, sample applications, notebooks, technical blogs, workshops, events, community, and technical launch support.
Develop original ways to show what Cerebras speed makes possible for developers.
Personally build, write, teach, present, and debug when needed.
Lead and coach DevRel ICs, give specific feedback, and maintain quality
Turn developer and community feedback into actionable input for Product and Engineering.
Partner with New Media on technical storytelling and launches, and with Product, Engineering, and Communications on technical accuracy and developer readiness.
Measure success through developer activation, continued usage, and useful ecosystem engagement.
Strong technical builder who can use APIs, create useful demos, and debug integrations.
Excellent technical writer, teacher, and presenter who makes complex ideas clear and accurate.
A distinct, informed point of view on modern DevRel and the AI developer ecosystem.
Strong judgment about which audiences, use cases, and programs deserve investment, with clear reasons for what to decline.
Able to do the work expected of the DevRel team and improve other people’s work.
Strong people management, cross-functional collaboration, and prioritization under constraints.
A strong portfolio of technical demos, applications, tutorials, workshops, or developer programs, with clear examples of personal technical contribution.
Experience leading a DevRel function or technical developer program with demonstrated results in developer adoption.
Experience directly managing and developing technical contributors, including setting expectations, giving actionable feedback, and addressing performance gaps.
A track record of delivering technical launches or developer programs with Product, Engineering, and creative partners.
Experience turning developer insights into improvements to products, documentation, or the developer experience.
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:
Build a breakthrough AI platform beyond the constraints of the GPU.
Publish and open source their cutting-edge AI research.
Work on one of the fastest AI supercomputers in the world.
Enjoy job stability with startup vitality.
Our simple, non-corporate work culture that respects individual beliefs.
Find out more about what it's like to work at Cerebras here!
Apply today and become part of the forefront of groundbreaking advancements in AI!
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.
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Job details are sourced from the employer's original posting.
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Cerebras Systems is building a new class of computer to accelerate artificial intelligence work by training neural networks much faster than conventional systems.