Engineering Manager, Machine Learning
- San Francisco, CA, USA
- Employees can work remotely
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.
Square's Machine Learning Platform team has one goal: Make machine learning at Square easy. Square has over 100 engineers and data scientists building machine learning solutions; our team's job is to support these efforts. We build serving systems to ensure that those trying to use machine learning can spend less time on infrastructure and more time delivering value. To accomplish this, we build scalable systems that can serve our customers needs. Our systems have to be tough enough to stand up to the high scale load of processing all payments at Square; furthermore, they have to be flexible enough to allow data scientists to do their job. We sit in a unique spot: your team will be an infrastructure one, but we work directly with Data Scientists daily.
- You will directly lead and empower an outstanding team of engineers by developing your team, promoting engineering decision-making, and applying your technical expertise to constantly improve the team and us.
- Help set the vision to guide us into the next generation of an online machine learning platform and establish practices to address customer needs.
- The main job of the team is to evaluate models to make the Data Scientist's job easy in production. We need someone with experience in production systems or experience that supports the vast array of ML requirements that can serve models built through any DS tool.
- You will learn and understand multiple complex technical problems facing our multiple product, data science, and analyst teams and design reusable infrastructure to solve their problems and use the commonalities between them.
- 5+ years professional experience building large software projects
- 2+ year(s) managing engineering teams
- 2+ years Machine Learning experience
- Excellent software engineering knowledge and the ability to provide technical mentorship and guidance
- Experience handling complex technical concepts, working with remote teams to make decisions that allow them to move forward, and communicating those decisions upward.
- Experience working with product managers, data analysts, and other engineering and leaders.
Technologies we use:
- Java, Python, Google Cloud Platform, AWS, Snowflake, Docker
- Python ML stack (pandas, scikit-learn, Jupyter, etc.)
- MySQL, Redis, Hibernate, jOOQ, Bigtable
At Square, we value diversity and always treat all employees and job applicants based on merit, qualifications, competence, and talent. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us by sending an email to assistance(at)squareup.com. We will treat your request as confidentially as possible. In your email, please include your name and preferred method of contact, and we will respond as soon as possible.
At Square, we want you to be well and thrive. Our global benefits package includes:
- Healthcare coverage
- Retirement Plans
- Employee Stock Purchase Program
- Wellness perks
- Paid parental leave
- Paid time off
- Learning and Development resources