We are hiring a Staff Machine Learning Engineer to revolutionize how we optimize our operations using ML and GenAI. This is a unique opportunity to apply the latest advances in ML and GenAI to real-world problems that directly impact millions of users and the future of digital finance.
Our AI teams are dedicated to building intelligent systems that protect our platform while ensuring a seamless experience for legitimate users. We are transforming traditional tasks such as manual document review and risk assessment into an automated, scalable system powered by state-of-the-art machine learning.
What you'll be doing:
- Design and implement multi-modal ML models that can understand and extract information from various document types (IDs, proof of address, etc.)
- Fine tune LLMs for automated document processing and risk assessment.
- Design model for ascertaining user onboarding risk and triggering Know-Your-Customer and Enhance Due Diligence models.
- Create real-time ML pipelines that can detect and prevent risks before they materialize
- Work with experts to translate their domain knowledge into ML features and models
- Build explainable ML systems that can justify their risk assessments
- Collaborate with platform teams to deploy models at scale with high availability and low latency
What we look for in you:
- 8+ years of industry experience in Machine Learning (or PhD+5)
- MS in Machine Learning, Computer Science, other technical field (PhD preferred)
- 10x developer with ability to leverage auto-code generation techniques for ML and scalable distributed applications.
- Strong foundation in modern ML techniques (DNNs, transformers, LLMs, classification)
- Experience building and deploying production ML systems at scale.
- Ability to balance ML model complexity with production requirements
- Strong communication skills to work effectively with domain experts
Nice to have:
- Background in fraud detection or risk modeling
- Familiarity with regulatory requirements in financial services
- Knowledge of crypto/blockchain technology
Job ID: GPML06US