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 improving over time.

If developing customer-facing ML-driven product features fascinates you, we are looking for ML engineers to join the team and help deliver the future of Square for Retail.

You will...

• Play a key role in developing ML-driven product features that help millions of Sellers worldwide

• Develop data pipelines, engineer features, and train ML models for NLP, computer vision, and other use cases

• Build and maintain low-latency, high-RPS ML services

• Work cross-collaboratively with data scientists, product managers, and designers to iterate on and define product functionality and UX design

• Partner closely with platform and product teams to review engineering designs and provide architectural guidance

You have…

• Excellent understanding of ML modeling techniques, from "classic" statistical classifiers to "modern" deep learning and transformer approaches

• Excellent command of engineering fundamentals

• Experience with NLP and/or computer vision use cases a plus

• Motivation to build beautiful, intuitive products 

• Innate curiosity, and a desire to learn and teach

• Empathy for your customers and colleagues

• Eagerness to share your own ideas, and openness to those of others

Languages and tech frameworks we use (and teach)...

• PyTorch, TensorFlow, Keras; scikit-learn, SpaCy, Hugging Face; Spark

• Python, Java, JavaScript, Go, Ruby

• MySQL, Redis, Kafka, Elasticsearch, PostgreSQL, Cassandra, Memcached

• AWS, GCP

• Healthcare coverage
• Retirement Plans
• Employee Stock Purchase Program
• Wellness perks
• Paid parental leave
• Paid time off
• Learning and Development resources