Build scalable LLM systems with Python and PyTorch at Cerebras, applying machine learning research to high-performance training and inference workflows.
As an Applied Machine Learning Research Scientist, you will turn modern machine learning techniques into production-ready systems. You will work with researchers and senior engineers across LLM pretraining, fine-tuning, reinforcement learning-based post-training, and evaluation. The work emphasizes hands-on engineering, model quality, data strategies, and reliable implementation at scale.
Key Responsibilities:

Apply RLVR, RLHF, and GRPO post-training techniques
Build evaluation pipelines across tasks and domains
Debug data, training, model, and precision issues
Scale pipelines across LLM development stages
Optimize training and inference workflows
Requirements:

Bachelor’s or Master’s degree in a related field
Four-plus years working with machine learning systems
Strong Python programming skills
Experience with PyTorch or comparable ML frameworks
Understanding of transformers and machine learning fundamentals
Apply machine learning, reinforcement learning, and systems expertise to advance Cerebras LLM training and deployment.

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