Senior Runtime Engineer

Cerebras Systems; United States; On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Equal opportunity employer

Continuous learning and growth opportunities

Job summary

Cerebras Systems is seeking to hire a talented software engineer to develop high-performance distributed systems that manage large-scale execution workloads. The role focuses on creating software that pushes the limits of concurrency and scalability, ensuring efficient execution of models across diverse hardware platforms.

Applicants should have at least 3 years of experience in distributed system software, proficiency in C/C++, and a strong foundation in multi-threading and performance optimization.

The position offers an innovative work environment where you can contribute to state-of-the-art AI technology.

Qualifications

• 3+ years of experience developing high-performance or distributed system software.
• Strong expertise in multi-threading and memory management.
• Proven debugging and optimization skills across scales.

Responsibilities

• Design and implement distributed runtime components for large-scale execution.
• Optimize data and communication pipelines fully utilizing resources.
• Collaborate with ML teams to integrate new model architectures.

Skills

C/C++ programming skills

Multi-threading

Performance optimization

Distributed systems

Memory management

Education

Bachelor’s or Master’s in Computer Science or Electrical Engineering

Tools

Profiling and instrumentation tools

Python

PyTorch

Job description

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

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.
• Bachelor’s, Master’s, or equivalent experience in Computer Science, Electrical Engineering, or related field.

Preferred Skills & Qualifications

• Familiarity with machine learning training or inference pipelines, especially distributed training and large‑model scaling.
• Exposure to Python and PyTorch, particularly in the context of model training or performance tuning.
• Experience with compiler internals, custom hardware interfaces, or low‑level protocol design.
• Prior work on high‑performance clusters, HPC systems, or custom hardware/software co‑design.
• Deep curiosity about how to unlock new levels of performance for large‑scale AI workloads.

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around us.

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