AIML Researcher/Engineer, Foundation Model Post-Training

What you'll do

• Design and iterate on end-to-end post-training strategies including Reinforcement Learning.
• Pioneer novel algorithms for preference optimization, model steering, and safety.
• Research methods for high-quality human and synthetic data generation and automated filtering.
• Implement curriculum learning to improve model instruction following and reasoning.
• Design robust evaluation methodologies to measure model helpfulness, factuality, and utility.
• Translate user requirements into specific model capabilities for product integration.
• Inform architecture choices by partnering closely with pre-training teams.

What we're looking for

• Demonstrated expertise in deep learning with a focus on LLMs, post-training, or reinforcement learning.
• Proficient programming skills in Python and a major deep learning framework such as JAX or PyTorch.
• Masters/PhD, or equivalent practical experience, in Computer Science, Machine Learning, or a related technical field.
• Experience training state-of-the-art large models at scale with familiarity in distributed training challenges (preferred).
• Experience improving model performance on complex reasoning tasks such as math, coding, and logic (preferred).
• Experience with various transformer architectures and its transformations (preferred).
• Strong communication skills and a passion for working cross-functionally across Research and Product teams (preferred).