Senior Machine Learning Engineer - Fraud (Research Scientist)

Plaid2 weeks ago

Location

United States

Type

Full Time

Salary

USD 180,000 – 250,000

Level

Senior

Role

Machine Learning Engineer

Posted

Feb 28, 2026

Full TimeSenior

The role

Summary

Plaid is seeking a Senior Machine Learning Engineer specializing in fraud detection to lead advanced research and development of cutting-edge ML models. The ideal candidate will prototype state-of-the-art fraud detection solutions using complex data modalities and transform research insights into production-ready systems that protect millions of financial transactions.

What you'll do

Advanced Fraud Detection Research: Build next-generation fraud detection capabilities by researching and prototyping state-of-the-art machine learning methods across graph ML, sequential modeling, and multimodal learning.

Research Roadmap Execution: Own and drive a research roadmap that translates academic research and prototypes into measurable product impact for Plaid's fraud prevention systems.

Technical Communication: Publish applied research findings and collaborate across Data, Product, and Engineering teams to elevate Plaid's fraud machine learning capabilities.

Large-Scale Data Insights: Work with one of the largest financial datasets to generate insights that help protect and empower millions of consumers' financial experiences.

What we look for

Technical

Machine Learning ExpertiseAdvanced proficiency in developing and implementing machine learning models, with strong emphasis on Graph Neural Networks and Transformer-based foundation models.

Programming SkillsStrong Python programming skills with ability to build high-quality research prototypes and production-ready code.

Data ModelingExperience with large-scale training, graph systems, and sequential modeling techniques.

Education

Advanced DegreePhD in Machine Learning, Computer Science, or related field strongly preferred; equivalent research experience will be considered.

Experience

Research ExperienceMinimum of 3+ years as a Machine Learning Engineer or Research Scientist with a strong publication/innovation track record.

Domain ExpertisePreferred experience in fraud, security, or abuse prevention domains.

Skills

Required skills

PythonAdvanced programming skills for developing machine learning prototypes and production systems

Machine Learning ResearchAbility to design and execute rigorous ML experiments and evaluation methodologies

Scientific CommunicationStrong technical writing and presentation skills for internal and external research communication

Nice to have

Graph Neural NetworksExperience with advanced graph-based machine learning architectures

Multimodal LearningExpertise in handling diverse data types including relational graphs, sequential events, images, and video

Fraud DetectionBackground in developing machine learning solutions for financial security and fraud prevention

Compensation & benefits

Salary

USD 180,000 – 250,000 (annual)

Stock options

Available

Benefits

Health Insurance

Comprehensive medical, dental, and vision coverage

Equity Compensation

Stock options and competitive equity grant program

Professional Development

Funding for conferences, research publications, and continuous learning opportunities

Remote Work Flexibility

Option for remote work with potential relocation support

Research Support

Resources and support for publishing academic research and attending technical conferences

Interview process

1
Initial Screening — Phone or video call with recruiting team to discuss background and initial fit
2
Technical Assessment — Take-home machine learning research challenge or technical screening focused on ML capabilities
3
Research Presentation — Candidate presents past research work and approach to solving complex ML problems
4
Onsite/Virtual Interviews — Multiple rounds of interviews with research scientists, engineering managers, and cross-functional team members
5
Final Interview — Discussion with senior leadership about research vision and potential contributions to Plaid's fraud detection capabilities