Staff ML Scientist / Engineer

We are looking for a strong technical contributor with a background in machine learning and systems engineering to join our team. As a Staff ML Scientist / Engineer, you will be helping us create an entirely new network for the world's money. You will be working on a highly autonomous role, identifying low-hanging fruit worth tackling to move us ever closer to the vision you create for the problem-space you own.

About the Role: Our team exists to enable hundreds of specialized risk ML models – their lifecycle, governance and interface with compliance – while removing as much of the maintenance toil from Data Scientist and product teams. We split the work in two tracks: Labels and Models.

• Labels: central platform for risk labels
• Models: libraries and services to experiment, eval, promote and track models

You will be contributing to:

• Software engineering: e.g. testing + CI/CD, monitoring/alerting + disaster recovery
• MLOps: Terraform and AWS infra, ML governance for hundreds of models
• Data Engineering: distributed processing at terabyte scale
• Science: drive experiments to prove value of new methodologies / algorithms applied to cross-team domains

Qualifications:

• Extensive experience with end-to-end distributed data systems, specially ML-centric ones;
• Previous experience as Data/ML Scientist/Engineer in large scale product;
• Excellent Software Engineering skills. Ability to work with Python and Java;
• Strong drive to solve problems for Data Scientists, high agency in complex scenarios;
• Ability to build incrementally, think from first principles and prioritize simpler solutions;
• Good communication skills with technical and non-technical audiences, to engage and manage project stakeholders;
• Strong problem solving skills with the ability to help refine problem statements and propose solutions taking effort-impact-scalability tradeoff into account;

Some skills that will make you stand out:

• Apache Spark, Iceberg, Kafka, dbt, GraphFrames, Ray
• Scikit-Learn, XGBoost, PyTorch, MLFlow, Graph ML
• AWS (S3, EMR, SageMaker, Lakeformation)
• Terraform, Docker, GitHub CI/CD
• A/B testing. ML explainability