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Machine Learning Intern (PhD)

Plaid

Plaid

Software Engineering
San Francisco, CA, USA
Posted on Monday, September 25, 2023
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.
Plaid’s data science team is building models that improve how millions of users understand and grow their financial lives. Our data scientists apply state-of-the-art machine learning and modeling techniques -- including natural language processing, anomaly detection, optimization, and time series forecasting -- toward different product areas. We value not only technical know-how, but also creativity, user empathy, and teamwork.
In this role, you will focus on improving the performance of Plaid’s machine learning model performance, feature engineering, stability, and coverage. You will lead the efforts to experiment with new modeling approaches and strategies, and will be collaborating closely with a skilled team of engineers on ingesting signals and productionizing these models at scale.

Responsibilities

  • Build products with real impact: your work will touch tens of millions of end-users, the best applications in FinTech, and major U.S. financial institutions
  • Passionate about applying machine learning at scale to real-world problems
  • Designing and interpreting experiments to measure the impact of new features on Plaid

Qualifications

  • Deep understanding of statistical techniques and modern machine learning techniques and their mathematical models, such as classification, clustering, deep neural network and natural language processing
  • Strong product intuition and excitement to work fast and iteratively
  • Strong familiarity with Python, and ability to code and iterate independently on top of data infrastructure tools like Jupyter notebooks, standard ML libraries, Spark, etc
  • Master/PhD degree in Data Science, Computer Science, Mathematics, Statistics, Operations Research, Economics, or a closely related field graduating in either December 2024 or May 2025.
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 accommodations@plaid.com.
Please review our Candidate Privacy Notice here.