Applied AI/ML Scientist

Responsibilities

• Collaborate with customer stakeholders to identify AI approaches for business problems and define technical project scopes.
• Architect and execute end-to-end training recipes for custom models, tailoring architectures to meet specific performance requirements.
• Design and implement adaptation strategies including continuous pre-training, supervised fine-tuning (SFT), and post-training alignment (RLHF/DPO).
• Manage the full training pipeline, including data preprocessing, tokenization, hyperparameter tuning, and loss-curve analysis.
• Scale training workloads across Cerebras clusters to ensure efficient utilization for multi-billion parameter models.
• Build and optimize core components for agentic systems, focusing on tool-use, long-context reasoning, and multi-step planning.
• Serve as a technical subject matter expert for customers and act as the "voice of the customer" for internal R&D and engineering teams.

Requirements

• Master's or PhD in Computer Science, Machine Learning, or a related field.
• Expert-level understanding of modern model architectures, including dense Transformers, MoEs, and multimodal models.
• Proven track record of training and/or fine-tuning large-scale models with 1B+ parameters.
• Mastery of Python and PyTorch.
• Experience with distributed training frameworks and large-scale distributed data processing pipelines.
• Strong interpersonal skills with the ability to present complex technical results to diverse audiences, from researchers to C-level executives.

About the Company

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, delivering industry-leading training and inference speeds. Cerebras empowers machine learning users to effortlessly run large-scale ML applications without the hassle of managing hundreds of GPUs or TPUs.