Software Engineer

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.

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).