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. We’re working to find new and better ways to help businesses succeed on their own terms—and we’re looking for people like you to help shape tomorrow at Square.

As a Machine Learning Tech Lead within the Risk Machine Learning team, you will guide the technical strategic direction of our machine learning efforts as it pertains to risk. This enables a software-driven, machine learning centric view on all money movement and every transaction within the growing Square seller ecosystem. Developing a technical, strategic perspective around active maximization of the trade off of revenue growth and risk using artificial intelligence that spans detection, reinforcement learning, experimentation, and other topics falls under such scope. The machine learning software that we release works with every transaction and money movement within our seller ecosystem - a profound degree of impact. Such machine learning techniques touch on reinforcement learning, decision theory, deep learning sequence modeling, and optimization theory. We also aim to provide our sellers, through seller facing products, with transparency around why our machine learning made a decision. This touches on algorithms in the relatively new space of explainable artificial intelligence.

Our algorithms obtain value from our unique and rich data from our entire product portfolio within our growing seller ecosystem. We partner with business, product, operations and engineering teams to promote optimal decision making systems using modeling and machine learning. We're a passionate team of entrepreneurs, scientists, and engineers who are shipping machine learning software that manages Square's view on each transaction as it pertains to our revenue growth and risk.

You Will:

• Lead and drive cross functional machine learning projects from beginning to end: build relationships with partner teams, other machine learning leaders across Square/Cash, frame and structure questions, collect and analyze data, research, prototype, and build machine learning pipelines, and runtime services of models in production.

• Decompose a complex product area and develop technical themes to work with several teams on the execution of such. Ownership of several machine learning projects will also be part of the role in addition to the aforementioned technical thesis across a variety of work streams.

• Strong machine learning / software development experience in Python plus experience with tensorflow, pytorch, and sklearn.

• Expert level knowledge of modern techniques in machine learning and deep learning, e.g., transformer network architectures, with an orientation to maximizing such algorithms in a large-scale production setting. 

• Have Reinforcement learning experience for developing control policies.

• Collaborate with business leaders, subject matter experts, and decision makers to develop success criteria and optimize new products, features, policies, and models

• Have a strategic perspective of how several machine learning solutions come together against a team, technical product strategy, and lead to an optimal sequence of work.

You Have:

• 10+ years industry experience in software engineering or machine learning-focused roles.

• An advanced degree (M.S., PhD.), in Computer Science, Electrical Engineering, Statistics, Physics, Mathematics or a related technical field.

• Experience performing machine learning model development and deep learning in practice using Python (numpy, tensorflow, pytorch, scikit-learn) and SQL/NoSQL interaction patterns.

• Familiarity with Linux/OS X command line, version control software (git), and general software development principles with a machine learning software development life-cycle orientation.

• Machine learning strategic sequencing of methodological and software improvements to work back from maximizing core metrics associated with optimizing the business.

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