Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs, delivering industry-leading training and inference speeds.
Cerebras works with leading model labs, global enterprises, and AI-native startups.
About the role
As an Applied AI Scientist in the FieldML team, you will develop and customize large language models and large-scale deep learning models to solve specific customer problems.
You will bridge the gap between state-of-the-art research and real-world applications using the Cerebras Wafer-Scale Engine (WSE).
Responsibilities
Collaborate with customer stakeholders to identify the best AI approaches to their business problems.
Contribute to technical scoping: feasibility analysis, data quality/availability assessments, and model architecture selection.
Define project milestones, success metrics, and evaluation benchmarks.
Architect and execute end-to-end training recipes for custom models.
Design and implement adaptation strategies: continuous pre-training, supervised fine-tuning (SFT), and post-training alignment via RLHF or DPO.
Own the training pipeline from data preprocessing and tokenization to hyperparameter tuning and loss-curve analysis.
Scale training workloads across Cerebras clusters for multi-billion parameter models.
Build and optimize components of agentic systems: tool-use, long-context reasoning, multi-step planning.
Serve as an AI/ML subject matter expert during technical deep-dives.
Requirements
Master's or PhD in Computer Science, Machine Learning, or related fields.
Expert understanding of modern model architectures: dense transformers, MoEs, multimodal and sequence models, scaling laws and training dynamics.
Proven track record training and/or fine-tuning large models (1B+ parameters).
Mastery of Python and PyTorch, experience with distributed training frameworks and large-scale data processing pipelines.
Strong communication skills, able to present complex technical results to diverse audiences.