Senior Machine Learning Engineer, Risk Modeling

Responsibilities

• Partner with product, engineering, data science, policy, and operations to design and productionize ML-driven risk solutions at scale
• Own end-to-end machine learning systems — from problem definition and modeling to deployment, monitoring, and iteration
• Lead technical decision-making within your workstreams and influence ML strategy and planning
• Build tooling and processes that improve the speed, reliability, and impact of the ML development lifecycle
• Apply state-of-the-art modeling techniques and third-party data sources to improve detection and decision-making
• Investigate emerging fraud, abuse, and risk patterns to proactively inform product safeguards and policy
• Collaborate closely with ML platform and engineering teams to ensure models operate reliably in real time and at scale

Requirements

• 8+ years of industry experience in machine learning, applied AI, or related fields
• Bachelor’s degree in a quantitative field (Computer Science, Engineering, Statistics, Physics, Applied Math); Master’s or PhD preferred
• Proven experience independently designing, deploying, and maintaining ML solutions in production
• Strong familiarity with techniques such as tree-based models, deep learning, transfer learning, or reinforcement learning
• Experience influencing technical direction and collaborating with cross-functional partners at scale
• Strong communication skills, sound judgment, and an ownership mindset

Technologies

• Python (NumPy, Pandas, scikit-learn, XGBoost, PyTorch, TensorFlow/Keras)
• PySpark, MLflow, workflow orchestration tools (Airflow, Prefect)
• GCP (Vertex AI), AWS
• Snowflake, MySQL, Tableau, Mode
• Containerization, CI/CD, and production ML best practices