Advanced Technology: AI/ML Research Scientist
Designs novel AI models and training methodologies from first principles on wafer-scale hardware, integrating computational science techniques. Requires PhD-level expertise in ML or related fields, strong publication record, and proficiency in PyTorch/Python.
About the job
What You Will Do
- Design AI models and training methods from first principles, leveraging architectural properties of wafer-scale hardware that are unavailable on conventional platforms.
- Investigate how techniques from computational science—numerical methods, PDE solvers, simulation—can inform and advance AI model design, and explore hybrid workflows that couple simulation and learning.
- Develop a deep understanding of the hardware substrate and use it to guide algorithmic choices: model structure, optimization strategy, memory access patterns, numerical precision.
- Publish findings and present at top-tier venues (NeurIPS, ICML, ICLR, etc.); represent Cerebras in the broader AI/ML research community.
- Inform the design of future Cerebras hardware and software by identifying the computational patterns that matter most for next-generation AI workloads.
What We Are Looking For
- PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field preferred; exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply.
- Mathematical maturity: comfort with the theory behind gradient methods, loss landscapes, generalization, and the relationship between model structure and data statistics.
- Track record of published research at top-tier AI or computational science venues.
- Proficiency in Python and PyTorch; comfort with C or other low-level languages is a strong signal.
- Excellent communication and interpersonal skills: able to present complex technical material to both ML and systems audiences, and to collaborate effectively in a fast-paced, small-team environment.
Skills
PyTorch, Python, C, Machine Learning, Optimization Theory, Numerical Methods, Pde Solvers, Gradient Methods, Wafer-Scale Hardware, Computational Science
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