AI/ML Research Scientist

Responsibilities

• Design AI models and training methods from first principles, leveraging the unique architectural properties of wafer-scale hardware.
• Investigate how computational science techniques, such as numerical methods and PDE solvers, can advance AI model design.
• Develop a deep understanding of the hardware substrate to guide algorithmic choices, including model structure, optimization strategy, and memory access patterns.
• Publish research findings and present at top-tier venues such as NeurIPS, ICML, and ICLR.
• Inform the design of future Cerebras hardware and software by identifying critical computational patterns for next-generation AI workloads.

Requirements

• PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.
• Mathematical maturity with a strong grasp of gradient methods, loss landscapes, and generalization.
• A track record of published research in top-tier AI or computational science venues.
• Proficiency in Python and PyTorch.
• Knowledge of C or other low-level programming languages.
• Excellent communication skills with the ability to present complex technical material to both ML and systems audiences.

About the Company

Cerebras Systems builds the world's largest AI chip, providing the compute power of dozens of GPUs on a single wafer-scale device. Our architecture enables industry-leading training and inference speeds, empowering users to run large-scale ML applications effortlessly. From top model labs to global enterprises, Cerebras is transforming the AI landscape with groundbreaking hardware and software.