Software Engineer - New Grad 2026

About the role

As a New Graduate Software Engineer, you will collaborate with world-class engineers to solve real-world challenges across the software stack. 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. You will gain hands-on experience working across multiple layers of a fully integrated AI-accelerated system, including advanced hardware interfaces, low-level infrastructure, distributed systems, compilers, and ML frameworks.

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.
• Contribute to low-level software components interacting closely with hardware and networking infrastructure.
• Learn and contribute across multiple layers of a fully integrated AI-accelerated platform.
• Participate in debugging, performance optimization, and system bring-up activities.
• Develop tools and infrastructure to improve observability, reliability, and scalability.
• Work cross-functionally with hardware, firmware, compiler, and infrastructure teams.
• Required Qualifications
• Recently graduated or currently enrolled in a university program in Computer Science, Computer Engineering, Electrical Engineering, or a related discipline (graduating in 2026).
• Proficiency in C/C++ programming languages
• Interest or exposure to systems/socket programming, networking, embedded systems, operating systems, device drivers, distributed systems, or network performance.
• Desire to work close to hardware/network and learn low-level engineering concepts.
• Detail-oriented but keen to learn the bigger picture and step out of comfort zone.
• Excellent communication and collaboration skills.
• Hybrid role based in Toronto, ON, or Sunnyvale CA
• Assets
• Experience with Linux systems programming or debugging tools.
• Familiarity with TCP/RDMA protocols, RPCs, and packet trace tools such as Wireshark
• Exposure to networking concepts, device drivers, embedded systems, or distributed infrastructure.
• Familiarity with performance optimization or concurrent programming concepts.
• Interest in large-scale AI infrastructure and accelerated computing systems.
• Why Join Cerebras
• 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.
• Read our blog: Intern at Cerebras
• Apply today and become part of the forefront of groundbreaking advancements in AI!

Additional Information

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top 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. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.

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