Cerebras Systems7h ago

Applied Machine Learning Research Scientist

Sunnyvale Ca Or Toronto Canadamid

Data Science

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Quick Summary

Overview

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,

Technical Tools

Data Science

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.

About the Role

~1 min read

As an Applied Machine Learning Research Scientist at Cerebras, you will play a key role in turning modern machine learning techniques into scalable, high-performance systems. This role sits at the intersection of modeling and systems focused not on publishing new algorithms, but on understanding how they work and making them run effectively at scale. Your work will directly impact how large language models (LLMs) are trained, optimized, and deployed on one of the most advanced AI platforms in the world.

You will work closely with researchers and senior engineers to implement and improve workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. This includes building training pipelines, debugging complex system behaviors, improving model quality, and iterating on data and evaluation strategies. Your contributions will help translate cutting-edge ML ideas into reliable, production-ready systems that solve real-world problems.

This role is ideal for candidates who enjoy hands-on engineering, want to build deep intuition for ML systems, and are excited about working on LLMs and reinforcement learning in practice, not just in theory.

Responsibilities

~1 min read

→Experience working with large language models (training, fine-tuning, and evaluation).
→Familiarity with reinforcement learning concepts.
→Experience with distributed training frameworks (e.g., FSDP, Megatron).
→Experience working with large-scale datasets and data pipelines.
→Experience debugging or optimizing ML systems for performance.
• Contributions to meaningful codebases, projects, or open-source systems

What We Offer

~1 min read

✓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.

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 them.

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