Staff/Senior Staff AI Engineer, Model Post-Training and Alignment

Confirmed live in the last 24 hours

OKX

Compensation

$313,055 - $450,000/year

San Jose, California, United States

On-site

Posted March 20, 2026

Job Description

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.

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.
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