AIML Researcher/Engineer, Foundation Model Post-Training

What you'll do

• Design and refine post-training strategies to enhance model behaviors using reinforcement learning techniques.
• Develop innovative algorithms for optimizing preferences, steering models, and ensuring safety in AI systems.
• Lead data strategy initiatives by researching methods for generating high-quality human and synthetic training datasets.
• Create robust evaluation frameworks to measure model performance in helpfulness, factuality, and utility beyond static benchmarks.
• Collaborate with pre-training teams to guide architectural decisions that align with user-centric requirements.

What we're looking for

• Demonstrated expertise in deep learning with a focus on LLMs, post-training, or reinforcement learning.
• Proficiency in Python and a major deep learning framework such as JAX or PyTorch.
• Master's degree, 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 their transformations (preferred).
• Strong communication skills and a passion for working cross-functionally across Research and Product teams (preferred).