Applied AI/ML Scientist - Sunnyvale, CA, United States

Originally posted 22 October 2026 by the employer.

Train and adapt multi-billion parameter models using the Cerebras Wafer-Scale Engine (WSE).

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 address specific customer problems. You will bridge research and production by training, fine-tuning, and optimizing models on the Cerebras Wafer-Scale Engine (WSE).

What you'll do

- Collaborate with customer stakeholders to define technical scoping, data readiness, model architectures, and evaluation benchmarks. - Architect and execute end-to-end training pipelines, including data preprocessing, tokenization, continuous pre-training, supervised fine-tuning (SFT), and post-training alignment via RLHF or DPO. - Analyze loss dynamics, gradient stability, and model convergence while scaling training workloads across Cerebras clusters for multi-billion parameter models. - Design and build agentic system components focusing on tool-use capabilities, long-context reasoning, and multi-step planning. - Serve as a technical AI/ML expert in customer deep-dives, translating business requirements into precise training recipes. - Partner with internal R&D and ML engineering teams to optimize software stack hardware utilization and document solutions into reusable playbooks.

What you'll need

- Master’s or PhD in Computer Science, Machine Learning, or a related field. - Expert-level understanding of modern model architectures, including dense transformers, MoEs, multimodal models, sequence models, scaling laws, and training dynamics. - Proven track record of training and/or fine-tuning large models (1B+ parameters) and addressing large-scale training challenges. - Mastery of Python and PyTorch. - Experience with distributed training frameworks and large-scale distributed data processing pipelines.