Staff AI Engineer, Model Post-Training and Alignment

San Jose, California, United States•San Jose, United States (US)

Remote

Senior

Full Time

about 1 month ago

💰$313,055 - $450,000

AIMachine LearningLarge Language ModelsPost-TrainingReinforcement LearningModel Alignment

Requirements

•Bachelor's in Computer Science, AI, Machine Learning, or related fields with at least 8 years of industry experience.
•Strong hands-on experience across the full post-training pipeline for large models.
•Deep familiarity with preference learning and alignment techniques, including DPO, GRPO, and RL-based post-training methodologies.
•Proven experience designing domain-specific data strategies and training methodologies.
•Experience training and post-training specialized small models from scratch.
•Solid understanding of reinforcement learning fundamentals and their application to model alignment.
•Experience deploying models in low-latency production environments using frameworks such as vLLM, SGLang, or similar.

•Lead and execute the full post-training pipeline for large language models (LLMs), including supervised fine-tuning, preference optimization, and reinforcement learning–based methods.
•Design and implement advanced training paradigms such as DPO (Direct Preference Optimization) and GRPO (Generalized Reward Policy Optimization).
•Develop domain-specific data recipes, curation strategies, and augmentation pipelines to optimize task performance.
•Conduct post-training of specialized small models from scratch, including architecture selection, dataset construction, and optimization strategy.
•Build and refine Reward Models to support alignment and downstream optimization.
•Design and implement RLAIF (Reinforcement Learning from AI Feedback) closed-loop systems.
•Optimize inference efficiency and deploy models using low-latency serving frameworks such as vLLM and SGLang.
•Evaluate model performance using both automated benchmarks and human/AI feedback loops.
•Collaborate with research and infrastructure teams to productionize training and deployment workflows.