Backed by Index, Kleiner Perkins and NEA.
About the role
Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersection of machine learning, data infrastructure, and production reliability.
What they're looking for
- 6+ years of relevant experience, with a strong focus on building, deploying, and scaling production machine learning systems
- Strong experience with ML infrastructure and operations, including production deployment, monitoring, and reliability
- Proven ability to independently own and deliver complex, end-to-end machine learning engineering projects
- Proficiency with Python and experience with ML and data technologies such as PyTorch, Spark, SageMaker, and Airflow
More about this role
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, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud...
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