Applied Machine Learning Research Scientist
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Summary
Applied ML research scientist at Cerebras who builds and scales ML workflows for LLM pretraining, fine-tuning, and reinforcement-learning post-training — day to day this means building evaluation and data pipelines, debugging across the ML stack, and optimizing training and inference. Core stack: Python, PyTorch, and transformer-based deep learning.
You will implement scalable ML workflows for LLM pretraining, fine-tuning, and reinforcement learning post-training. You will build evaluation and data pipelines, debug issues across the ML stack, optimize training and inference, and contribute maintainable infrastructure code.
Responsibilities
- Apply post-training techniques to improve model performance
- Build and maintain model evaluation pipelines
- Debug data pipelines, training jobs, model outputs, and lower-precision computation
- Translate ML ideas into scalable implementations
- Design and scale ML pipelines for pretraining, fine-tuning, and alignment
- Generate, filter, and use large datasets and synthetic data
- Optimize training and inference workflows for performance, efficiency, and reliability
- Contribute maintainable code to shared ML infrastructure
Requirements
- Machine learning
- Python
- PyTorch
- deep learning
- transformer
- ML paper implementation
