Hardware Research Analyst

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 a Hardware Technical Analyst with deep expertise in AI hardware or software to produce original research and turn complex technical developments into clear, credible content. You’ll investigate how systems work, test claims through demos and benchmarks, and explain what the results mean for developers and the broader industry.

This is a hands-on individual contributor role combining technical research, experimentation, and writing. You should be as comfortable examining an architecture or building a reproducible test as you are shaping an article and defending its conclusions. You’ll work closely with Developer Relations, Engineering, and New Media to find meaningful stories, validate the details, and help people understand a rapidly changing AI ecosystem.

What You’ll Own

• Original research: Analyze AI hardware, software, infrastructure trends, and industry announcements. Identify meaningful developments and differentiated story angles, and develop a point of view supported by evidence.
• Technical writing: Write articles and research that make complex ideas accessible without sacrificing accuracy. Explain how systems work, what is new, and why it matters.
• Demos and benchmarks: Build demos and run reproducible benchmarks to test claims, investigate performance, and explore tradeoffs. Document your methodology, assumptions, and limitations.
• Evidence and analysis: Synthesize technical and financial information from primary sources, expert conversations, and hands-on work into clear insights. Evaluate source quality and revise conclusions when the evidence changes.
• Technical collaboration: Partner with Developer Relations, Engineering, and New Media to develop technically grounded content, validate conclusions, and incorporate expert and editorial feedback.
• Industry engagement: Represent Cerebras at industry conferences and turn relevant findings and conversations into useful research and content.
• Research workflows: Use AI tools to accelerate research, coding, and writing while independently verifying sources, results, and claims.

Skills and Qualifications

• A strong portfolio of externally published technical writing, research, or other substantive technical content.
• Deep expertise in AI hardware or software, including the architectural and system tradeoffs that affect performance and practical use. Your strength may be in hardware architectures, compute, memory, and interconnects, or in models, inference, and AI application workflows.
• Demonstrated experience building demos or running benchmarks, with the ability to explain your personal contributions, methodology, and results.
• Clear technical writing and communication. You can explain complex mechanisms, support a point of view with evidence, and make the significance clear to your audience.
• Strong research judgment. You can evaluate sources, distinguish evidence from assumptions, design fair comparisons, and communicate limitations honestly.
• Good editorial judgment about what makes technical content original, useful, and engaging.
• The ability to learn unfamiliar technical subjects, collaborate with experts, incorporate critical feedback, and deliver under a deadline.
• Strong familiarity with AI tools and sound judgment about where independent verification is necessary.

Preferred Experience

• Experience combining technical and financial analysis to explain developments in AI infrastructure.
• Experience turning conference findings or conversations with technical experts into published research.