Deep Learning Quant Researcher

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.

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.

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 train models, debug systems, and deploy strategies in production with ultra‑low latency.

Conduct rigorous experiments, tune hyperparameters, backtest models against historical and real‑time data, and evaluate performance in dynamic market conditions (e.g., accounting for structural changes from events like elections or regulations).

Stay at the cutting edge of AI research by adapting open‑source tools (e.g., PyTorch, Hugging Face) and contributing to internal libraries for efficient training and inference.

Mentor junior team members and present findings to drive firm‑wide innovation in automated trading.

PhD (or equivalent experience) or Masters in Computer Science, Machine Learning, Statistics, Physics, Mathematics, or a related highly quantitative field.

Strong research track record in deep learning, AI, or quantitative modeling, ideally demonstrated through publications, projects, or prior industry experience.

Proficiency in probability, statistics, time‑series analysis, NLP, pattern recognition, and machine learning frameworks (e.g., PyTorch, Tensor Flow).

Experience with programming in Python, C++, or similar languages for implementing mathematical models and algorithms.

Familiarity with data‑driven research environments, including handling large, noisy datasets and distributed computing.

Intellectual curiosity, rigor, and a passion for applying AI to solve complex, real‑world problems in low‑signal‑to‑noise environments.

Background in quantitative finance, trading algorithms, or high‑frequency trading.

Expertise in reinforcement learning, generative models, or LLM applications in predictive tasks.

Experience with GPU‑accelerated computing, CUDA kernels, or scaling ML models on clusters (e.g., thousands of high‑end GPUs).

Prior work in collaborative settings, such as research labs or trading desks, where models influence live systems.

Ability to thrive in a…