Applied AI/ML Scientist

Cerebras Systems • Sunnyvale, CA

14h

About The Position

The Applied AI/ML Scientist will join Cerebras Systems’ FieldML team to develop and customize large language models and other large-scale deep learning models that address specific customer problems. This is a hands-on role bridging state-of-the-art research and real-world applications by helping customers use the Cerebras Wafer-Scale Engine (WSE). Projects may include training bespoke models from scratch, fine-tuning and optimizing large language models for industry verticals, and building components for custom agentic systems.

The role involves customer use-case discovery and project scoping, end-to-end model training and adaptation, and technical customer leadership. The scientist will also collaborate with Cerebras research, engineering, ML, and product teams to improve the software stack and hardware utilization, and to turn successful customer engagements into internal playbooks.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a related field.
• Expert-level understanding of modern deep-learning architectures, including dense transformers, mixture-of-experts (MoEs), multimodal and sequence models, scaling laws, and training dynamics.
• Proven experience training and/or fine-tuning large models with 1B+ parameters, including direct experience with large-scale training challenges.
• Mastery of Python and PyTorch.
• Experience with distributed training frameworks and large-scale distributed data-processing pipelines and tools.
• Strong communication and interpersonal skills, including the ability to present complex technical results to audiences ranging from executives to research scientists.
• Ability to work autonomously and collaboratively in a fast-paced, dynamic environment, manage multiple projects, and adapt as customer needs evolve.

Responsibilities

• Collaborate with customer stakeholders to identify AI approaches for their business problems.
• Contribute to engagement scoping, including feasibility analysis, data quality, availability and readiness assessments, and model architecture selection.
• Define project milestones, success metrics, and evaluation benchmarks.
• Architect and execute end-to-end training recipes for custom models, tailoring architectures and recipes to customer performance and accuracy requirements.
• Design and implement continuous pre-training, supervised fine-tuning, and post-training alignment strategies such as RLHF or DPO.
• Own training pipelines from data preprocessing and tokenization through hyperparameter tuning and loss-curve analysis.
• Analyze model convergence, loss dynamics, and gradient stability on specialized hardware.
• Scale training workloads across Cerebras clusters and efficiently use hardware for multi-billion-parameter models.
• Build and optimize agentic-system components focused on tool use, long-context reasoning, and multi-step planning.
• Serve as an AI/ML subject-matter expert in technical deep-dives and translate customer requirements into training recipes.
• Build and maintain strong customer relationships.
• Represent customer needs to internal R&D and engineering teams to drive software-stack and hardware-utilization improvements.
• Partner with internal ML and product teams on model architectures, training recipes, and internal case studies.
• Convert successful customer projects into internal playbooks for the FieldML team.

Benefits

• Opportunity to build a breakthrough AI platform beyond GPU constraints.
• Opportunity to publish and open-source cutting-edge AI research.
• Opportunity to work on a high-speed AI supercomputer.
• Job stability with startup vitality.
• A simple, non-corporate work culture that respects individual beliefs.
• A work environment focused on continuous learning, growth, and support.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior