Senior Runtime Engineer

About the role

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 https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About The Role

We are building the next generation of large-scale AI systems that power training and inference workloads at unprecedented scale and efficiency.

You will design and develop high-performance distributed software that orchestrates massive compute and data pipelines across heterogeneous clusters. Your work will push the limits of concurrency, throughput, and scalability—enabling efficient execution of models at massive scale. This role sits at the intersection of systems engineering and machine learning performance, demanding both architectural depth and low-level implementation skills. You will help shape how models are executed and optimized end-to-end, from data ingestion to distributed execution, across cutting-edge hardware platforms.

We’re hiring for runtime roles across both Training and Inference.

Responsibilities

- Design and implement distributed runtime components to efficiently manage large-scale execution workloads.

- Develop and optimize high-performance data and communication pipelines that fully utilize CPU, memory, storage, and network resources.

- Enable scalable execution across multiple compute nodes, ensuring high concurrency and minimal bottlenecks.

- Collaborate closely with ML and compiler teams to integrate new model architectures, training regimes, and hardware-specific optimizations.

- Diagnose and resolve complex performance issues across the software stack using profiling and instrumentation tools.

- Contribute to overall system design, architecture reviews, and roadmap planning for large-scale AI workloads.

Skills & Qualifications

- 3+ years of experience developing high-performance or distributed system software.

- Strong programming skills in C/C++, with expertise in multi-threading, memory management, and performance optimization.

- Experience with distributed systems, networking, or inter-process communication.

- Solid understanding of data structures, concurrency, and system-level resource management (CPU, I/O, and memory).

- Proven ability to debug, profile, and optimize code across scales—from threads to clusters.