Cerebras

AI Engineer, Developer Relations

Hybrid

Part remote, part office, you need to live within commuting distance of a named location.

Hybrid · Sunnyvale, CA

Employer listed it 5 days ago · Added 4 days ago

Listed for 1 day before it closed.

This job has closed

Cerebras has taken this listing down, so it is no longer accepting applications. The details below are kept for reference only.

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Salary

Not stated

Location

Hybrid · Sunnyvale, CA

Timezone

Not stated

Contract

Full-time

Experience

Mid

What the advert says

Quoted word for word from the employer's advert. Where it says nothing, we say so.

Working hours: Not mentioned in the advert
Where you can work from: Location eligibility not confirmed
Contract: Not mentioned in the advert
Experience asked for: Not mentioned in the advert
Pay: Not mentioned in the advert

Remote flexibility

Hybrid

This role is only partly remote, the employer expects time in the office around Sunnyvale, CA, Hybrid, so you need to live within commuting distance.

What the employer says

Source listing states candidate location: "Sunnyvale, CA, Hybrid"
Listing mentions "Hybrid"

What Nomaders makes of it

Not suitable if you plan to move between countries

The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.

Before you apply

Experience: employer states

Written as a nice-to-have

“Preferred Experience”

Questions worth asking: Nomaders interpretation

This role involves office time, but the advert does not say how often. Ask how many days a week are expected on site.
No pay is stated. Ask for the salary range before investing time in interviews.
The advert does not say whether this is employment or a contractor arrangement. Ask, as it affects tax and benefits.

Produced automatically from this advert's text; not reviewed by a person. The employer's own page is the final word.

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 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 Developer Relations 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.

Requirements

·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.