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Staff Machine Learning Engineer

San Jose, California, United States

Who We Are

At OKX, we believe that the future will be reshaped by Crypto, ultimately contributing to every individual's freedom. OKX began as a crypto exchange giving millions of people access to crypto trading and over time becoming among the largest platforms in the world. In recent years, we have developed one of the most connected Web3 wallets used by millions to access decentralized crypto applications (dApps). OKX is a trusted brand by hundreds of large institutions seeking access to crypto markets on a reliable platform that seamlessly connects with global banking and payments. In the last year, OKX has expanded into new markets including Australia, Brazil, Netherlands, Singapore and Turkey, with plans to launch in the US, Belgium and the UAE.
 
We are deeply committed to shaping a fairer, more transparent and accessible society through blockchain technology. This is why we publish proof of reserves monthly, and continue to ship new innovative security features.

About the Opportunity

We are seeking a highly skilled and experienced Senior and Staff Machine Learning Engineers to join our Risk Engineering Team. The ideal candidate will be adept in developing and implementing advanced machine learning models to enhance our capabilities in fraud detection, for example bot detection, credit card chargeback prevention, promotion abuse protection and so on.

What You’ll Be Doing 

  • Design, develop, and deploy machine learning models to detect and prevent fraudulent activities.
  • Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
  • Optimize existing machine learning systems for performance and scalability.
  • Collaborate in the architecture and design of data pipelines and infrastructure to support machine learning workflows.
  • Conduct research and implement new machine learning techniques and methodologies.
  • Mentor junior team members and contribute to knowledge sharing within the team.

What We Look For In You

  • At least 5+ years of experience in Machine Learning Engineering. 
  • Proficiency in Python and familiarity with Java.
  • Solid understanding of common machine learning models, including experience with frameworks like LightGBM and XGBoost.
  • Experience with SQL and familiarity with common data products such as PostgreSQL, DynamoDB, Kafka, and Redis.
  • Knowledge of at least one neural network framework, such as TensorFlow.
  • Experience in building and maintaining data pipelines.
  • Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related field.
  • Strong problem-solving skills and ability to work in a fast-paced environment.
  • Excellent communication and collaboration skills.

Nice to Haves

  • Experience in fraud detection, specifically in areas like bot detection, credit card chargeback prevention, and promotion abuse protection, is highly desirable.

Perks & Benefits 

  • Competitive total compensation package
  • L&D programs and Education subsidy for employees' growth and development
  • Various team building programs and company events

OKX Statement

The salary range for this position is $126,000 to $240,000. The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via OKX internal or external careers site.
 
OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider employment-qualified applicants with arrest and conviction records.
 

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