Applied Machine Learning Research Scientist

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 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 

• Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance.

• Build and maintain evaluation pipelines to measure model performance across tasks and domains.

• Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation.

• Collaborate with researchers to translate ML ideas into efficient, scalable implementation.

• Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment).

• Work with large datasets, including dataset generation, filtering, and synthetic data approaches.

• Optimize training and inference workflows for performance, efficiency, and reliability.