Member of Technical Staff Reinforcement Learning

You will design, develop, and optimize reinforcement-learning training pipelines for diffusion-based LLMs. You will build and evaluate reward models, fine-tune and scale generative AI models, process data, evaluate models, and improve the stability, efficiency, and reproducibility of RL workloads.

Responsibilities

• Design, develop, and optimize RL training pipelines for diffusion-based LLMs
• Build and iterate on reward models, reward-shaping strategies, and reward-quality evaluation
• Implement approaches for fine-tuning and scaling generative AI models
• Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases
• Research and implement controlled text-generation and constraint-satisfaction techniques
• Improve the stability, efficiency, and reproducibility of RL workloads

Requirements

• BS, MS, or PhD in Computer Science or a related field, or equivalent experience
• At least 2 years of experience working on ML projects in PyTorch or equivalent
• Familiarity with transformers and core LLM concepts
• Experience with RLHF, PPO, DPO, or related post-training methods
• Familiarity with training and inference in diffusion models
• Experience training deep-learning models at scale in distributed computing environments

Benefits

• Equity
• Flexible vacation and paid time off
• Health, dental, and vision insurance
• 401k match
• Catered meals
• Commuter subsidies