Machine Learning Anti-Fraud Engineer
What you'll do:
- This role requires Software Engineering expertise to develop and deploy highly scalable algorithms that are used by thousands of users globally.
- We work cross-functionally with other engineers, product teams, business partners, and customers across a wide range of groups within Zoom to identify problems, define solutions, execute plans, measure results, and communicate these results to our partners.
- We have end-to-end responsibility for our solutions to these problems, from initial investigation, to solution proposal, to implementation and long-term maintenance.
- Collaborate with cross-functional teams to develop AI-based solutions to prevent unwanted abuse of Zoom services, including Zoom Phone, Zoom Meetings, OnZoom, and other future products
- Extract and process real-time and batch data from Zoom services for data exploration, aggregation, and validation
- Research, experiment, and evaluate statistical and machine learning techniques to develop fraud detection systems that satisfy business requirements and security policies
- Train, evaluate, and validate fraud detection models using mainstream cloud computing platforms
- Serve trained fraud detection models as prediction services into the production environment for on-line (streaming) and off-line (batch) inferences
- Follow MLOps best practices to develop and maintain machine learning pipelines that can automate the retraining and deployment of new fraud detection models
- MS or PhD in Computer Science, Engineering, Statistics, or similar fields with at least 3+ years of working experience with production-scale ML systems
- Advanced proficiency in Java or Python
- Proven experience with common ML libraries, such as Scikit-learn, TensorFlow, PyTorch, Keras
- Strong fundamentals in statistics and machine learning
- Knowledge of and experienced with cloud computing and big data frameworks, such as AWS, GCP, Spark, Flink, or Beam
- Proficient in standard software development, such as version control, debugging, testing, and deployment
- Ability to use MLOps frameworks such as TensorFlow Extended, Kubeflow, or MLFlow
- Demonstrated problem-solving mindset with analytical skills and attention to detail
- MS or PhD in Computer Science, Engineering, Statistics, or similar fields with at least 3 years of working experience with production-scale ML systems
- A track record in conceptualizing and building end-to-end big data frameworks
- Peer-reviewed publications in conferences or journals
- Experience in enterprise fraud prevention or telecommunications risk management
- Language: English, Mandarin is a plus
Ensuring a diverse and inclusive workplace where we learn from each other is core to Zoom’s values. We welcome people of different backgrounds, experiences, abilities and perspectives including qualified applicants with arrest and conviction records as well as any qualified applicants requiring reasonable accommodations in accordance with the law.
We believe that the unique contributions of all Zoomies is the driver of our success. To make sure that our products and culture continue to incorporate everyone's perspectives and experience we never discriminate on the basis of race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status.
All your information will be kept confidential according to EEO guidelines.
Given Zoom’s status as a federal contractor, we are subject to President Biden’s Executive Order requiring COVID-19 vaccinations for all US employees. As such, Zoom requires all US employees, including remote employees, to be fully vaccinated. Zoom will consider requests for reasonable accommodations for religious or medical reasons as required under applicable law.
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