Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity 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. What You’ll Be Doing 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. What We Look For In You 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. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process!

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