ML Software Engineer - Integration & Quality - New Grad

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.

We are looking for a new graduate or early-career Software Engineer to join the ML Integration and Quality team at Cerebras. This team works at the intersection of machine learning infrastructure, distributed systems, and hardware/software co-design. In this role, you will help integrate, test, and validate the software stack that powers the Cerebras AI platform. You will work alongside experienced engineers across runtime, compiler, kernel, infrastructure, and hardware teams to investigate issues, build automation, and improve the reliability of large-scale machine learning workloads. This is an excellent opportunity for an early-career engineer who enjoys solving technical problems, learning how complex systems work, and gaining hands-on experience with large-scale AI infrastructure.

Help integrate and validate software components across the Cerebras AI platform. Develop automated tests and tools that support software integration, system validation, and release quality. Investigate software failures, test regressions, and unexpected system behavior with support from senior engineers. Collaborate with engineers across ML runtime, compiler, kernel, infrastructure, and hardware teams. Contribute to the development and maintenance of testbeds used to validate system functionality, performance, and reliability. Help reproduce, document, and troubleshoot issues across different layers of the ML software stack. Contribute to test plans and validation strategies for new features and platform capabilities. Improve internal tooling, diagnostics, logging, and debugging workflows. Analyze test results and system data to identify failure patterns, edge cases, and opportunities for improvement. Participate in code reviews, technical discussions, and team development processes. Learn about distributed systems, machine learning infrastructure, and hardware/software interactions while contributing to production software. Minimum Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Software Engineering, Electrical Engineering, or a related technical field. Strong programming fundamentals in Python, C++, Go, Java, or a similar language. Understanding of core computer science concepts, including data structures, algorithms, operating systems, or computer architecture. Experience debugging software through internships, research, or co-op placements. Familiarity with software development practices such as version control, unit testing, and code reviews. Strong analytical and problem-solving skills. Interest in working across multiple areas of a complex software system. Ability to communicate clearly and collaborate effectively within a technical team. Curiosity, attention to detail, and a willingness to learn new technologies. Preferred Skills & Qualifications Internship, co-op, research, or project experience in software engineering, systems engineering, infrastructure, or machine learning. Exposure to machine learning frameworks such as PyTorch, TensorFlow, or JAX. Familiarity with Linux development environments and command-line tools. Exposure to distributed systems, networking, cloud infrastructure, or large-scale compute environments. Experience building test automation,