Lead research and development of advanced fraud detection ML models, experiment design, productionizing prototypes, and mentoring engineers.
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
The Data team within Plaid’s Fraud organization builds the machine learning systems behind Plaid’s next-generation fraud detection products. Leveraging Plaid’s unique network data, the team develops end-to-end solutions to identify and prevent fraud before it happens. This includes ownership across the full ML lifecycle, from large-scale data processing and model experimentation to feature pipelines, model serving, and ongoing performance monitoring.
As a Senior Machine Learning Engineer (Research Scientist) you will lead applied research to develop next-generation fraud detection models across complex data modalities, including relational graphs, sequential events, images, and video. You will design and run rigorous experiments and build evaluation methodologies that reflect real-world fraud dynamics, prototype state-of-the-art architectures such as Graph Neural Networks and Transformer-based foundation models, and partner closely with Machine Learning Engineers to translate successful research into production systems. The role also involves communicating and publishing results internally and externally, helping raise the technical bar for fraud machine learning at Plaid.
***We are open to remote candidates for this role***
Responsibilities
- Build next-generation fraud detection capabilities by researching and prototyping state-of-the-art methods across graph ML, sequential modeling, and multimodal learning.
- Owning a research roadmap that ships: moving from papers/prototypes to measurable product impact.
- Publishing applied research and collaborating with a high-caliber team across Data, Product, and Engineering.
- Working with one of the largest financial datasets to generate insights that help hundreds of millions of consumers achieve greater financial freedom.
Qualifications
- PhD strongly preferred; we will consider equivalent research experience with a strong publication/innovation track record.
- 3+ years of experience as a Machine Learning Engineer or Research Scientist.
- Strong scientific rigor and communication.
- Strong Python skills + ability to build high-quality research prototypes.
- Fraud / security / abuse domain experience is a plus.
- Experience with large-scale training, graph systems, and sequential modeling expertise is a plus.
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. 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. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at [email protected].
Please review our Candidate Privacy Notice here.
Top Skills
Graph Neural Networks
Python
Transformer-Based Models
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