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Plaid is looking to stop fraud before it happens by building machine learning systems that harness the scale and richness of Plaid's network data to protect consumers and businesses. The company aims to improve the accuracy and effectiveness of fraud detection systems and explore GenAI-driven capabilities.

Requirements

• 3-5 years total experience, with at least 2 years of hands-on work in ML systems, modeling, or data engineering
• Proven experience building and deploying end-to-end machine learning system
• Strong foundation in Python and core ML principles
• Demonstrated curiosity and adaptability — comfortable working across both modeling and infrastructure
• experience in fraud detection, risk modeling, or related domains
• familiarity with large language models (LLMs) or generative AI frameworks

Responsibilities

• Build and deploy end-to-end ML solutions — from feature engineering to production deployment
• Scale and optimize machine learning systems in a real-world, high-traffic environment
• Explore and apply cutting-edge LLMs and generative AI to strengthen fraud prevention and investigation
• Develop new features and machine learning models that enhance the accuracy and effectiveness of our fraud detection systems
• Build reliable data and model pipelines to power both experimentation and production workflows
• Design and deliver scalable, high-quality ML systems that protect Plaid’s customers at scale
• Explore and prototype GenAI-driven capabilities that push the boundaries of our fraud modeling and investigation tools

Other

• Grow your career in a fast-paced, collaborative environment
• We recognize that strong qualifications can come from both prior work experiences and lived experiences.
• We encourage you to apply to a role even if your experience doesn't fully match the job description.
• We are always looking for team members that will bring something unique to Plaid!
• Plaid is proud to be an equal opportunity employer and values diversity at our company.