Machine Learning Engineer, Commerce Platform
Company Name:-
Square
Job Location:-
Atlanta, GA
Job Summary:-
Job detailsJob TypeFull-timeFull Job DescriptionCompany Description
Square builds common business tools in unconventional ways so more people can start, run, and grow their businesses.
When Square started, it was difficult and expensive (or just plain impossible) for some businesses to take credit cards.
Square made credit card payments possible for all by turning a mobile phone into a credit card reader.
Since then Square has been building an entire business toolkit of both hardware and software products including Square Capital, Square Terminal, Square Payroll, and more.
Were working to find new and better ways to help businesses succeed on their own termsand were looking for people like you to help shape tomorrow at Square.
Job Description
The Commerce ML team applies machine learning to improve the experience of our sellers and help automate the running of their business.
In particular, the team focuses on shipping ML-driven features for Square for Retail, in areas such as helping offline sellers get online more easily, setting their catalogs up more quickly, and optimizing their inventory levels.
To develop each feature, we pay close attention to four key and interdependent aspects: Design, Modeling, Engineering, and Analytics.
Design is concerned about the usefulness and remarkability of the feature, and thus cares about the overall functionality, ease of use, and aesthetics of the experience.
Modeling is concerned about the accuracy of the ML model, and thus cares about the training data, the features and performance of the model, and crucially for a customer-facing product how the application behaves in the face of the mistakes the model will inevitably make (e.
g.
, false positives, false negatives, lack of predictions above a certain confidence).
Engineering in turn is concerned about running the ML model at scale, and thus cares about the latency, throughput, and robustness of the inferencing service.
Finally, Analytics is concerned about the adoption of the feature, and thus cares about the instrumentation to capture detailed usage and acceptance rate, the definition of success metrics and dashboards, and the collection of feedback in a manner that the ML model can learn from, and thus keep impro
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