Top AI & Machine Learning Jobs

As a Machine Learning Engineer II at Affirm, you'll develop AI systems for automating customer operations and collaborate on building models while ensuring high-quality code production.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

Independently validate and monitor machine learning models used for credit underwriting, credit risk, and fraud detection. Identify weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Partner cross-functionally to implement and maintain the Model Risk Management framework and support audit, compliance, and regulatory requests.

Lead fraud monitoring and detection efforts, develop prevention strategies, analyze data to identify loss drivers, and mentor junior team members.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Lead a team of ML engineers to develop and improve fraud detection models, defining strategies and fostering collaboration across functions to ensure effective model integration.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Lead the development of fraud prediction models, build feature pipelines, prototype modeling ideas, and productionize models while collaborating with cross-functional teams.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.