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 that power Plaid’s cutting-edge fraud detection products. By leveraging Plaid’s extensive network data, we enable proactive fraud prevention—stopping fraud before it happens. Our team owns the entire ML lifecycle, from developing feature pipelines and training models to deploying and monitoring them in production. We ensure that our systems scale reliably and efficiently as Plaid continues to grow and support hundreds of customers.
As a Staff Machine Learning Engineer on Plaid’s Fraud Data team, you will design and build scalable ML infrastructure that powers our industry-leading fraud detection product. You’ll lead the evolution of our model deployment, monitoring, and observability frameworks to ensure high reliability and performance at scale. Collaborating closely with teams across ML Infrastructure, Product, and Engineering, you’ll deliver robust systems that protect users and customers from fraud. In addition, you’ll mentor other engineers and help shape the long-term technical vision and strategy of the Fraud Data team.
Responsibilities
- Design and build scalable ML infrastructure for Plaid’s fraud detection product
- Working at a fast-pace environment to build a rapidly growing product with a championship team
- Solving complex problems at the intersection of ML systems, data, and reliability
- Building the foundations for fraud detection on the largest financial dataset in the world
- Collaborating with talented engineers and data scientists across Plaid
Qualifications
- 8+ years total experience, with at least 5 years building and deploying production ML systems.
- Proven experience in machine learning infrastructure/operations.
- Demonstrated technical leadership and architectural vision, driving systems from concept to production.
- Proficiency in Python, PyTorch, Spark, SageMaker, and Airflow, or equivalent technologies.
- Nice to have - experience working in fraud detection, risk modeling, or financial security domains.
- Nice to have - background in graph machine learning or related techniques.
$253,200 - $400,000 a year
The target base salary for this position ranges from $253,200/year to $400,000/year in Zone 1. The target base salary will vary based on the job's location.
Our geographic zones are as follows:
Zone 1 - New York City and San Francisco Bay Area
Zone 2 - Los Angeles, Seattle, Washington D.C.
Zone 3 - Austin, Boston, Denver, Houston, Portland, Sacramento, San Diego
Zone 4 - Raleigh-Durham and all other US cities
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].
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