Senior ML Software Engineer - Integration & Quality

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 Software Engineer to join the ML Integration and Quality team s 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.

• 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.

• ~5 years of experience in software engineering, systems engineering, or infrastructure development.
• Strong programming skills in Python, C++, Go, or similar languages
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• Experience debugging complex systems or distributed software environments.
• Familiarity with systems-level development, infrastructure tooling, or platform integration
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• 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.

• 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.

• This role follows a hybrid schedule, requiring in-office presence 3 days per Please note, fully remote is not an option.
• Office locations:
  Sunnyvale, CA or Toronto, ON.

Find out more about what it's like to work at Cerebras here
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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 us.

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