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.
As a Senior Software Engineer in ML Integration and Quality team, you will play a pivotal role in bringing together and delivering all software and hardware components for Cerebras AI platform. You will focus on SW components feature integration and quality. Pre deployment/production validation for Cerebras training and inference solution. As part of this role, you will influence the best testing practice, good debugging methodology, effective cross team communication and advocate for world-class products.
Responsibilities- Develop and execute a comprehensive integration and QA strategy aligned with the roadmap of the Cerebras AI solution.
- Execute with good software integration methodology, collaborate with effective communication and ensure quality.
- Break down complex tasks into smaller tasks, be a problem solver and help debug
- Automation of workflows, testbed setups and building tools to monitor/debug.
- Implement creative ways to break Cerebras software and identify potential.
- Contribute to developing SW specifications with a focus on ML.
- Drive quality of various software and hardware components of Cerebras AI platform to ensure accuracy, performance and usability of ML training and inference.
- Ability to work in a fast-paced environment and make the necessary prioritizations and judgements which affects productivity at a company.
- Define and implement quality metrics to measure product and process quality, provide actionable insights and recommendations to drive continuous.
- Provide regular updates on quality, key metrics, and risks to engineering and business stakeholders.
- Collaborate with software and product team to develop clear acceptance criteria and deliver quality product.
- Execute and deliver with strong sense of ownership and quality driven.
- 5+ years of relevant industry experience in Software integration, development.
- Strong automation and programming skills using one or more programming languages like Python, C++ or go.
- Experience in testing compute/machine learning/networking/storage systems within a large-scale enterprise environment.
- Experience in debugging issues across distributed scale out.
- Experience in understanding complex systems and putting together thorough test-plans.
- Experience working effectively across teams, including product development, product management, customer operations, and field teams.
- Excellent verbal and written communication.
- Strong organizational skills, teamwork, and can-do attitude.
- Experience working with geographically dispersed teams across time.
- Experience in working with ML workloads such as LLM/Multimodal training or;
- Experience with hardware architecture, performance optimizations, compilers and ML frameworks.
- Experience working with distributed systems, cloud and;
- Experience working with microservices deployment, debugging.
- This role follows a hybrid schedule, requiring in-office presence 3 days per Please note, fully remote is not an option.
- Office locations: Sunnyvale, Toronto.
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/
