ashby
Senior ML Software Engineer - Integration & Quality
Cerebras Systems
Sunnyvale, USA
Full-time
Senior · 5+ years experience
Hybrid
Discovered 3 weeks ago
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Role Overview
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.
Key Skills for This Role
PythonC++GoLLM
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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.
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