Staff AI Engineer, Model Post-Training and Alignment

OKX

About The Position

We are seeking a highly skilled and hands-on Machine Learning Engineer specializing in large model post-training and alignment. This role focuses on designing, executing, and optimizing post-training pipelines to improve model performance, controllability, domain adaptation, and reasoning capabilities. You will work across the full lifecycle of post-training—from data strategy and reward modeling to reinforcement learning–based optimization and production-grade inference deployment.

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.

Responsibilities

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.