AI RLHF Systems Engineer vs AI Robustness Engineer

A AI RLHF Systems Engineer Only

• Deep understanding of reinforcement learning fundamentals (policy gradients, PPO, DPO, KTO)
• Reward model design, training, and evaluation for preference data
• Large-scale distributed training with multi-GPU / multi-node orchestration
• Preference data collection pipeline design including annotation quality assurance
• Prompt engineering and red-teaming for alignment evaluation
• Python proficiency with PyTorch, HuggingFace Transformers, and TRL
• Statistical analysis of human annotation data (inter-annotator agreement, bias detection)
• Experiment tracking, ablation studies, and reproducible ML workflows