Cerebras Systems
Software Engineer - New Grad 2026
Toronto Office
Sponsorship not specifiedDetected 71 days ago
C++Distributed SystemsLinuxMachine LearningComplianceEmbedded SystemsElectrical EngineeringWiresharkCommunicationCollaboration
About the role
- You will contribute to software systems that directly impact performance, scalability, reliability, and usability of next-generation AI infrastructure.
- This role is ideal for candidates with a strong interest in systems programming, networking, embedded systems, distributed infrastructure, or performance-oriented software engineering.
- Our teams work very closely with hardware, so candidates with experience primarily focused on higher-level application development or AI applications may be less aligned with the nature of this work.
Responsibilities
- Collaborate with experienced engineers on real-world systems and infrastructure challenges.
- Design, implement, test, and debug software solutions that directly impact system performance and reliability.
- Develop tools and infrastructure to improve observability, reliability, and scalability.
- As a New Graduate Software Engineer, you will collaborate with world-class engineers to solve real-world challenges across the software stack.
Requirements
- Proficiency in C/C++ programming languages
- Experience with Linux systems programming or debugging tools.
- Familiarity with TCP/RDMA protocols, RPCs, and packet trace tools such as Wireshark
- Familiarity with performance optimization or concurrent programming concepts.
Skills
- Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups.
Company info
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.
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This listing is sourced directly from Cerebras Systems's careers page and normalized into a canonical job model.