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.
Cerebras' current customers include top 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.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.
We are building the next generation of large-scale AI systems that power training and inference workloads at unprecedented scale and efficiency.
You will design and develop high-performance distributed software that orchestrates massive compute and data pipelines across heterogeneous clusters. Your work will push the limits of concurrency, throughput, and scalability—enabling efficient execution of models at massive scale. This role sits at the intersection of systems engineering and machine learning performance, demanding both architectural depth and low-level implementation skills. You will help shape how models are executed and optimized end-to-end, from data ingestion to distributed execution, across cutting-edge hardware platforms.
We’re hiring for runtime roles across both Training and Inference.
Responsibilities- Design and implement distributed runtime components to efficiently manage large-scale execution workloads.
- Develop and optimize high-performance data and communication pipelines that fully utilize CPU, memory, storage, and network resources.
- Enable scalable execution across multiple compute nodes, ensuring high concurrency and minimal bottlenecks.
- Collaborate closely with ML and compiler teams to integrate new model architectures, training regimes, and hardware-specific optimizations.
- Diagnose and resolve complex performance issues across the software stack using profiling and instrumentation tools.
- Contribute to overall system design, architecture reviews, and roadmap planning for large-scale AI workloads.
- 3+ years of experience developing high-performance or distributed system software.
- Strong programming skills in C/C++, with expertise in multi-threading, memory management, and performance optimization.
- Experience with distributed systems, networking, or inter-process communication.
- Solid understanding of data structures, concurrency, and system-level resource management (CPU, I/O, and memory).
- Proven ability to debug, profile, and optimize code across scales—from threads to clusters.
- Bachelor’s, Master’s, or equivalent experience in Computer Science, Electrical Engineering, or related field.
- Familiarity with machine learning training or inference pipelines, especially distributed training and large-model scaling.
- Exposure to Python and PyTorch, particularly in the context of model training or performance tuning.
- Experience with compiler internals, custom hardware interfaces, or low-level protocol design.
- Prior work on high-performance clusters, HPC systems, or custom hardware/software co-design.
- Deep curiosity about how to unlock new levels of performance for large-scale AI workloads.
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.
Read our blog: Five Reasons to Join Cerebras in 2026.
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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Top Skills
What We Do
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, functional business experts and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art.
The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2.
Join us: https://cerebras.net/careers/
