Applied AI/ML Scientist
Remote • Sunnyvale, CA • FullTime
Posted 4h ago
Job Location
Sunnyvale, CA
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
LLM Engineer
About the job
As an Applied AI Scientist in the FieldML team, you will be responsible for developing and customizing large language models and more broadly 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 by helping customers harness the power of the Cerebras Wafer-Scale Engine (WSE) for their AI initiatives. We are looking for experienced AI Scientists who are passionate about the "applied" side of machine learning, enjoying implementing, training, and scaling models to solve complex business and scientific problems. You will work on diverse projects, from training bespoke models from scratch to fine-tuning and optimizing LLMs for specific industry verticals, to designing and building components for custom agentic systems.
Responsibilities
- Collaborate with customer stakeholders to identify AI approaches for business problems.
- Contribute to technical scoping, including feasibility, data readiness, 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 like continuous pre-training, SFT, and post-training alignment (RLHF/DPO).
- Own the training pipeline, including data preprocessing, tokenization, hyperparameter tuning, and loss-curve analysis.
- Navigate model convergence on specialized hardware, analyzing loss dynamics and gradient stability.
- Scale training workloads across Cerebras clusters for multi-billion parameter models.
- Build and optimize core components of agentic systems, focusing on tool-use, long-context reasoning, and multi-step planning.
- Serve as an AI/ML subject matter expert during technical deep-dives.
- Build and maintain strong customer relationships.
- Act as the 'voice of the customer' for internal R&D and engineering teams.
- Partner with internal ML and product teams on novel model architectures and training recipes.
- Distill successful customer projects into internal playbooks.
Requirements
- Master's or PhD in Computer Science, Machine Learning, or related fields.
- Expert-level understanding of modern model architectures (dense transformers, MoEs, multimodal, sequence models), scaling laws, and training dynamics.
- Proven track record of training and/or fine-tuning large models (1B+ parameters).
- Direct experience with the challenges of large-scale model training.
- Mastery of Python and PyTorch.
- Experience with distributed training frameworks.
- Experience with large-scale distributed data processing pipelines and tools.
- Effective communication and interpersonal skills for collaborative and fast-paced environments.
- Ability to work autonomously and manage multiple projects.
- Ability to present complex technical results to diverse audiences.
Benefits
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Job stability with startup vitality.
- Simple, non-corporate work culture.