About the Role
We are looking for a Software Engineer to join the ML Integration and Quality team at Cerebras. This team sits at the intersection of machine learning infrastructure, distributed systems, and hardware/software co-design. In this role, you will help integrate and validate the software stack that powers the Cerebras AI platform, ensuring large-scale ML workloads run reliably and efficiently across our systems. You will work closely with engineers across runtime, compiler, kernel, and hardware teams to debug complex issues, improve automation, and strengthen the reliability of our AI infrastructure. This is an excellent opportunity for engineers who enjoy working across the stack, debugging complex systems, and improving the reliability of large-scale AI platforms.
Responsibilities
- Integrate and validate software components across the Cerebras AI platform.
- Collaborate with engineers across ML runtime, compiler, kernel, and hardware teams to ensure reliable feature integration.
- Investigate and debug complex issues across distributed systems and large-scale ML workloads.
- Build automation tools and infrastructure to support integration testing, system validation, and debugging workflows.
- Develop and maintain testbeds used to validate system performance and reliability.
- Identify system bottlenecks, failure points, and edge cases that impact ML workload performance.
- Contribute to test plans and validation strategies for new features and platform capabilities.
- Improve observability, diagnostics, and debugging workflows across the ML software stack.
- Work with product and engineering teams to ensure high-quality releases of the Cerebras inference platform.
Minimum Skills & Qualifications
- ~5 years of experience in software engineering, systems engineering, or infrastructure development.
- Strong programming skills in Python, C++, Go, or similar languages.
- Experience debugging complex systems or distributed software environments.
- Familiarity with systems-level development, infrastructure tooling, or platform integration.
- Experience building automation tools, testing frameworks, or internal developer tooling.
- Strong problem-solving skills and the ability to investigate issues across multiple system layers.
- Excellent communication and collaboration skills.
Preferred Skills
- Experience working with machine learning infrastructure or ML model deployment.
- Familiarity with LLM or multimodal model workloads.
- Experience with distributed systems, cloud infrastructure, or large-scale compute clusters.
- Exposure to performance debugging, profiling, or system observability tools.
- Experience with microservices, containerized environments, or cluster orchestration.
- Exposure to hardware accelerators, compilers, or ML frameworks.