Senior Machine Learning Engineer - Fraud (Research Scientist)
Plaid
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.
228960 - 344160 USD a year
The target base salary for this position ranges from $228,960/year to $344,160/year [in Zone 1, in Zone 4 or encompassing all Zones]. The target base salary will vary based on the job's location.
Our geographic zones are as follows:
Zone 1 - San Francisco / New York City / Seattle
Zone 2 - Los Angeles / Washington DC / Austin / Boston / Sacramento / San Diego
Zone 3 - Atlanta / Portland / Chicago / Philadelphia / Denver / Miami / Dallas / Raleigh
Zone 4 - All other US cities
The base salary range listed for this full-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training.