Block's AI R&D team is looking to transform the way the company detects fraudulent behavior by investing in and advancing technologies in Graph Representation Learning, Sequential Modeling, Explainability, and Sequential Decision Making.
Requirements
- 1-2 years of experience with applied Deep Learning
- Proven ability to implement in practice neural network architectures described in literature using deep learning frameworks such as PyTorch
- Python, Pytorch, Numpy, Pandas, SQL
Responsibilities
- Develop new, standardized, datasets, either for internal use or for external release
- Provide quantitative assessment of the advantages and disadvantages of different model classes based on accuracy, precision/recall, training cost, training time, data requirements, and interpretability
- Explore the mathematical foundations of various model classes to understand their representational ability
- Develop new algorithms or state of the art models for tasks of interest
- Communicate your project status and results frequently
Other
- This opportunity is only open to students who are currently enrolled in either a Masters or PhD program
- Clear communication skills
- A curious, passionate, growth-oriented mindset
- Demonstrated ability to lead a research project from ideation to publication
