Staff Machine Learning Engineer, OKX

• At least 5+ years of experience in Machine Learning Engineering
• Strong experience with ML Ops frameworks and tools (e.g., Flyte, Airflow, Kubeflow, MLflow, etc)
• Proficiency in Python and familiarity with Java
• In-depth knowledge of data validation, data pipeline development, and performance monitoring systems
• Extensive experience in deploying and managing machine learning models in production environments
• Solid understanding of CI/CD practices for machine learning workflows
• Experience with SQL and familiarity with common data products such as PostgreSQL, DynamoDB, Kafka, and Redis
• Familiarity with A/B testing, model drift detection, and advanced monitoring techniques
• Strong problem-solving skills and ability to work in a fast-paced environment
• Excellent communication and collaboration skills
• Proven experience coaching and mentoring junior engineers

Desirable

• We are seeking a highly skilled and experienced Senior/ Staff Machine Learning Engineers to join our Risk Engineering Team, focusing on fraud detection, for example bot detection, credit card chargeback prevention, promotion abuse protection and so on
• As a Tech Lead, you will be responsible for overseeing the end-to-end ML model development, deployment, and maintenance in production environments
• You will also play a crucial role in building and maintaining the data validation pipeline and model performance monitoring systems
• This position offers an exciting opportunity to lead a team of engineers while directly contributing to the growth of our machine learning infrastructure
• Lead the design, development, and deployment of end-to-end machine learning pipelines for production use, ensuring scalability, reliability, and performance
• Design and implement a robust model performance monitoring system to track and evaluate the success of models in real-time, identifying areas for improvement
• Manage and optimize the full lifecycle of ML models, including versioning, retraining, and monitoring model performance in production
• Collaborate with cross-functional teams to implement and improve data validation pipelines, ensuring the integrity and quality of data used in ML models
• Work closely with cross-functional teams to understand business requirements and translate them into technical solutions
• Provide technical leadership and mentorship to junior team members, supporting their professional development through coaching and code reviews