Deep Learning Quant Researcher

Confirmed live in the last 24 hours

OKX

Hong Kong, Hong Kong SAR

On-site

Posted March 18, 2026

Job Description

This role doesn't accept fresh graduate CVs unfortunately and is only open for experienced hire with at least around 5+ years of working experience with deep learning experience. Thank you for your interest!

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

As a Deep Learning AI Researcher, you'll join a dynamic team of researchers, engineers, and traders to develop and deploy state-of-the-art neural network models that drive predictive trading strategies. You'll tackle noisy financial datasets, optimize for low-latency environments, and innovate on architectures tailored for high-volume, low-signal markets. This role combines frontier AI research with practical application in quantitative finance, enabling you to iterate rapidly from concept to production. Expect to work with massive GPU clusters, petabytes of market data (encompassing tick-by-tick exchange feeds, order books, and on-chain analytics), and cross-disciplinary teams to solve some of the most challenging problems in trading.

What You’ll Be Doing

Invent and refine deep learning models (e.g., transformers, convolutional networks, RL agents) to predict market behaviors, optimize order execution, and enhance risk management.
Analyze vast quantities of financial market data using statistical techniques, machine learning, and AI to extract actionable patterns and signals.
Build custom architectures, optimizations, and tricks adapted for trading, drawing from the latest papers in LLMs, computer vision, RL, generative modeling, and distributed training.
Collaborate closely with quantitative traders, software engineers, and infrastructure teams to