Risk Control Algorithm Engineer Intern

About Bybit

Established in March 2018, Bybit is one of the fastest growing cryptocurrency derivatives exchanges, with more than 70 million registered users. We offer a professional platform where crypto traders can find an ultra-fast matching engine, excellent customer service and multilingual community support. We provide innovative online spot and derivatives trading services, mining and staking products, as well as API support, to retail and institutional clients around the world, and strive to be the most reliable exchange for the emerging digital asset class.Our core values define us. We listen, care, and improve to create a faster, fairer, and more humane trading environment for our users. Our innovative, highly advanced, user-friendly platform has been designed from the ground-up using best-in-class infrastructure to provide our users with the industry's safest, fastest, fairest, and most transparent trading experience. Built on customer-centric values, we endeavour to provide a professional, 24/7 multi-language customer support to help in a timely manner.

As of today, Bybit is one of the most trusted, reliable, and transparent cryptocurrency derivatives platforms in the space.

About the Role

We are seeking a skilled machine learning engineer with a security or risk control background to join our team. As a Risk Control Algorithm Engineer Intern, you will need to apply statistical and machine learning techniques for anomaly detection.

Job Description

Conduct data exploration and statistical analysis based on a deep understanding of business processes, and develop machine learning models to detect various business threats.

Develop anti-cheating, anti-fraud, and adversarial technologies using data mining, machine learning, deep learning, and unsupervised learning techniques to address business security issues.

Collaborate with product and technical teams to deploy models, ensuring continuous maintenance, iterative optimization, and product upgrades.

Drive algorithmic innovation, becoming an industry leader in blockchain risk control by working on areas such as on-chain graph mining, knowledge graphs, and adversarial game theory.

Requirements

Master’s/Ph.D. candidates in Computer Science, Mathematics, Statistics, Information Science, or a related field.

Proficient in data mining, statistical learning, and deep learning algorithms, with experience in at least one machine learning platform.

Skilled in Python, Scala, or similar programming languages, with strong coding skills and experience in at least one complete algorithmic modeling project.

Highly data-sensitive, self-driven in research, interested in security and risk control, and possesses strong communication and collaboration skills.

Preferred Qualifications:

Published papers in CCF B or higher conferences or journals.

Top 1% placement in modeling competitions, such as Alibaba Tianchi, Alibaba AI Security Contest, Kaggle, or KDD competitions.

Experience with open-source contributions, with a demonstrated influence and innovativeness in the field.