Applied Machine Learning Research Scientist

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