<div>Cerebras Systems builds the worlds 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 global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.</div>About The Role As a Software Engineer on the ML Integration and Ops team, you will be a critical force in improving software quality through advanced debugging and automation. You’ll work across the full Cerebras AI platform—spanning hardware, software, and ML workloads—to identify, isolate, and resolve complex issues. This role emphasizes deep systems understanding, creative problem-solving, and building AI-powered tools to automate diagnostics, validation, and workflows. Your ability to break down ambiguous problems and automate solutions at scale will directly impact the reliability and performance of our training and inference products. Responsibilities <ul> <li>Debug and resolve complex integration issues across the Cerebras AI platform, spanning ML, compiler, runtime, and hardware layers.</li> <li>Develop and deploy AI-enhanced debugging and validation tools to accelerate issue identification and resolution.</li> <li>Automate test generation, data capture, and diagnostics using scripting and intelligent systems.</li> <li>Create and execute robust validation plans for LLM and multimodal workloads in production-scale environments.</li> <li>Identify edge cases, stress failure modes, and proactively improve system resilience.</li> <li>Design and maintain CI/CD pipelines to ensure continuous integration, fast feedback, and early detection of regressions.</li> <li>Collaborate with product, software, and hardware teams to establish clear quality criteria and efficient handoffs.</li> <li>Drive adoption of debugging best practices and tools across teams.</li> <li>Contribute to continuous improvement by implementing quality metrics, automation pipelines, and actionable insights.</li> </ul> Skills And Qualifications <ul> <li>2+ years of experience in software integration, debugging, or quality engineering.</li> <li>Strong programming and automation skills in Python, C++, Go, or similar languages.</li> <li>Experience testing compute, machine learning, networking, or storage systems in large-scale environments.</li> <li>Solid understanding of system architecture (compute, networking, storage) and ML workloads.</li> <li>Proven ability to break down complex issues into root causes and scalable solutions across distributed or complex systems.</li> <li>Ability to understand complex systems and design comprehensive, effective test plans.</li> <li>Strong collaboration and communication skills across cross-functional teams.</li> <li>Experience collaborating with globally distributed teams across time zones.</li> </ul> Preferred Skills and Qualifications <ul> <li>Experience with ML workloads such as LLM or multimodal model training and inference.</li> <li>Familiarity with AI-driven tools, prompt engineering, or synthetic data generation for validation.</li> <li>Background in hardware architecture, performance optimization, compilers, or ML frameworks (e.g., PyTorch, TensorFlow).</li> <li>Experience with distributed systems, cloud environments, and microservices deployment and debugging.</li> <li>Knowledge of CI/CD pipelines, automated test infrastructure, and system-level benchmarking.</li> <li>Exposure to software/hardware co-design and scalable ML system integration.</li> </ul> Location <ul> <li>This role follows a hybrid schedule, requiring in-office presence 3 days per week. Please note, fully remote is not an option.</li> <li>Office locations: Sunnyvale, Toronto.</li> </ul><div><h4>Why Join Cerebras</h4> 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: <ol> <li>Build a breakthrough AI platform beyond the constraints of the GPU.</li> <li>Publish and open source their cutting-edge AI research.</li> <li>Work on one of the fastest AI supercomputers in the world.</li> <li>Enjoy job stability with startup vitality.</li> <li>Our simple, non-corporate work culture that respects individual beliefs.</li> </ol> Read our blog: <a href="https://www.cerebras.net/blog/5-reasons-to-join-cerebras" target="_blank">Five Reasons to Join Cerebras in 2025.</a> <h4>Apply today and become part of the forefront of groundbreaking advancements in AI!</h4> <hr /> 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. <hr /> This website or its third-party tools process personal data. For more details, click <a href="https://www.cerebras.net/privacy/" target="_blank">here</a> to review our CCPA disclosure notice.</div>

CerebrasSystems

ML Integration & Operations Software Engineer

CerebrasSystems