Senior Backend Engineer
Cerebras
Senior Backend Engineer
Cerebras
AI accelerator hardware replacing GPUs
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 hiring a senior backend engineer to build and scale the services that power Cerebras Inference Cloud. Your work will support the Developer Console and the broader backend systems that developers and enterprises rely on to access, manage, and operate inference workloads.
This is a hands-on role for a backend generalist who enjoys working across services, APIs, databases, and data pipelines. You’ll solve problems spanning usage tracking, billing, quotas, account management, request logs, metrics, and other core cloud capabilities. These systems require careful data modeling, reliable integrations, and thoughtful tradeoffs across correctness, latency, scale, and cost.
You’ll join a fast-moving team and take ownership of major platform capabilities, from defining the problem and designing the architecture to shipping and operating the solution. You’ll work closely with frontend, infrastructure, inference, and product teams to deliver reliable services and a cohesive customer experience.
What You’ll Do
Own critical backend systems. Take end-to-end responsibility for capabilities such as billing, usage metering, quotas, account management, request logs, and metrics.
Build services across Inference Cloud. Design and implement APIs, backend services, and data pipelines that support the Developer Console and other cloud systems.
Design for correctness and scale. Define service boundaries, data models, and processing patterns, making pragmatic tradeoffs across consistency, real-time and batch processing, performance, and cost.
Connect systems reliably. Build integrations between customer-facing workflows, internal services, and infrastructure, with clear contracts and robust handling of failures and retries.
Drive projects from idea to production. Turn ambiguous needs into concrete designs, break down the work, and deliver solutions that grow with customer usage.
Operate what you build. Improve observability, debugging, testing, and recovery; investigate production issues and address their underlying causes.
Lead through technical ownership. Guide design reviews, mentor engineers, and coordinate execution across teams.
Partner across engineering and product. Work with frontend engineers and other stakeholders to translate customer needs into dependable backend capabilities.
What We’re Looking For
Strong backend engineering fundamentals. Experience designing APIs, services, data models, and distributed systems, with a practical understanding of concurrency, consistency, caching, and failure handling.
A track record of ownership. You’ve designed, delivered, and operated complex production systems, taking responsibility for their behavior after launch.
Breadth and adaptability. You’re comfortable moving between backend domains, learning unfamiliar systems, and choosing the right approach for the problem.
Database and data-processing experience. You can design schemas, reason about query performance, and build reliable transactional and asynchronous workflows.
Sound technical judgment. You know when a simple solution is sufficient and when scale, correctness, or reliability requires a deeper investment.
Independent execution. You bring clarity to ambiguous problems, manage dependencies, and drive work forward.
High engineering standards. You care about correctness, maintainability, observability, and long-term system health.
Collaboration and influence. You communicate architectural tradeoffs clearly and help other engineers succeed through mentorship and constructive feedback.
Experience. Typically 8+ years of industry experience building and operating production software.
Education. Bachelor’s or master’s degree in computer science, or equivalent practical experience.
Relevant experience includes cloud platforms, developer tools, billing and metering systems, identity and access management, observability, or high-volume data processing. Experience with technologies such as GraphQL, Postgres, and Redis is helpful.
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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Cerebras Systems creates AI acceleration hardware and software. Its CS-2 system is designed to replace traditional GPU clusters for AI workloads, speeding up training and inference while simplifying the setup by eliminating the need for parallel programming, distributed training, and cluster management. The product works as a single, large processor-based accelerator with accompanying software and cloud services to run AI models efficiently, reducing latency and time to results. Compared with competitors, Cerebras differentiates itself with the largest processor in the industry and an integrated hardware-software stack that aims to streamline AI workflows rather than relying on multi-GPU clusters. The company's goal is to help research labs, healthcare, finance, and other industries achieve faster, more cost-effective AI development and deployment by offering a turnkey high-performance AI compute solution.
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