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.
The
Recommendation Team
plays a crucial role in optimizing user experiences by delivering relevant and engaging content in real-time. We develop and refine machine learning algorithms that power
content feeds, personalized recommendations, and user growth strategies.
Our work directly impacts user satisfaction and engagement on the OKX platform, helping drive business growth and content quality. We are looking for engineers
who are passionate about recommendation systems, machine learning, and large-scale AI applications.
What You'll Do:
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Develop and optimize large-scale recommendation algorithms
to improve personalized user experiences in content feeds, recommendations, and engagement-driven interactions.
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Implement and enhance retrieval, ranking, and re-ranking models
to refine recommendation precision and diversity.
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Apply sequence-based models
(e.g., Transformers, RNNs) to understand
user behavior over time
and improve personalization.
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Improve recall mechanisms
using collaborative filtering, content-based filtering, and deep learning-based vector search techniques.
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Leverage multi-modal learning
(text, image, graph) to enhance content understanding and personalization.
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Collaborate cross-functionally
with product managers, data engineers, and infrastructure engineers to refine recommendation strategies.
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Run A/B tests
to assess the performance of recommendation algorithms, analyze results, and iterate accordingly.
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Enhance system efficiency and scalability
, ensuring high-performance model deployment in real-world production environments.
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Stay updated with the latest research
in recommendation systems, machine learning, and AI to integrate cutting-edge innovations into production systems.
Qualifications:
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5+ years of experience
in recommendation systems, machine learning, or related AI fields. Senior candidates will be considered for the lead role.
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Hands-on experience
with
machine learning models
(collaborative filtering, matrix factorization, deep learning-based recommenders).
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Familiarity with retrieval, ranking, re-ranking, and cold-start problem solutions
in large-scale recommendation systems.
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Experience with deep learning frameworks
such as TensorFlow or PyTorch.
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Proficiency in big data processing
(Spark, Hive, Hadoop) and large-scale distributed computing.
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Understanding of user behavior modeling, multi-modal learning, and reinforcement learning
for recommendations.
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Strong analytical and problem-solving skills
with a passion for optimizing machine learning systems.
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Excellent communication skills
and the ability to work collaboratively with cross-functional teams.