Overview In this role you will design, build, and deploy ML models to detect and mitigate fraud across the AWS platform, leveraging massive datasets. You'll explore GenAI techniques to uncover new fraud patterns and strengthen defenses, collaborating with cross-functional teams to translate business needs into scalable solutions. You will iterate on detection strategies and metrics, monitor model robustness, and communicate insights to technical and non-technical stakeholders. This position offers the chance to influence fraud prevention at scale within a leading cloud platform.
Compensation / Benefits- sign-on payments and RSUs
- health insurance (medical, dental, vision)
- 401(k) matching
- paid time off
- parential leave
Responsibilities- Design, build, and deploy ML models to detect, prevent, and mitigate fraudulent activities across the AWS platform
- Analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and threats
- Explore GenAI techniques (LLMs, synthetic data generation, adversarial simulations) to enhance detection
- Collaborate with engineering, product, and operations teams to translate business needs into scalable solutions
- Experiment, prototype, and iterate on new detection strategies, algorithms, and metrics
- Monitor model performance and improve robustness against evolving fraud tactics
- Communicate findings clearly to technical and non-technical audiences
- Contribute to the broader fraud prevention strategy and best practices
Key requirements- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building models for business applications
- Experience programming in Java, C++, Python or related language
- Experience in state-of-the-art deep learning architectures, training/optimization, and model pruning
- Experience in algorithms, data structures, parsing, numerical optimization, data mining, parallel/distributed computing, HPC
- Cross-functional collaboration
- Clear technical communication to non-technical audiences
- Adaptability to evolving fraud threats
- Deep learning model design and optimization
- GenAI techniques (synthetic data generation, LLMs, adversarial simulations)
- Massive-scale data analysis
