Machine Learning Engineer, Automation

  • San Francisco, CA, United States
  • Full-time

Company Description

Since we first opened our doors in 2009, the world of commerce has evolved immensely – and so has Square. After enabling anyone to take a payment and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldn’t work together. So we expanded into software and started building integrated, omnichannel solutions – to help sellers sell online, manage inventory, run a busy kitchen, book appointments, engage loyal buyers, and hire and pay staff. And across it all, we’ve embedded financial services tools at the point of sale, so merchants can access a business loan and manage their cash flow all in one place.

Today, we’re a partner to sellers of all sizes – large, enterprise-scale businesses with complex commerce operations, sellers just starting out, as well as merchants who began selling with Square and have grown larger over time. As our sellers scale, so do our solutions. We all grow together.

There is a massive opportunity in front of us. We’re building a business that is big, meaningful, and lasting. And we are helping sellers around the world do the same.

Job Description

You will join a growing team of machine learning engineers in Square’s Automation organization which is a cross-functional team that accelerates Square sellers’ path to value realization by making technology investments that reduce friction and cost and increase personalization across customer interfaces.

Within the Automation org, the ML team works closely with Data Science, Data Engineering, Business Intelligence, Product Management and Software Engineering teams to inject intelligence and efficiency into Square’s direct communication paths with sellers.

The team works on a range of complex problems including but not limited to product recommendation systems, opportunity ranking, call transcripts modeling and predicting the best time and medium of contact. These models directly empower the Sales and Account Management organizations at Square to maximize their ROIs.

You will:

  • Drive end to end cross functional machine learning projects: you will build relationships with partner teams, frame problem statements, collect and analyze data, research & build prototypes, and finally productionize your ML solutions by designing and building their full pipelines

  • Strengthen your knowledge by using and learning a diverse set of techniques spanning all aspects of machine learning, causal inference, and other forms of statistical modeling to solve important business and product problems

  • Collaborate with business leaders, subject matter experts, and decision makers to develop success criteria and develop new ML & data products, features and procedures

  • Help build the next generation of data driven and intelligent products at Square


You have:

  • 5+ years of industry experience in ML or a PhD in a related field with 2+ years of experience

  • Fluency in Python and practical experience in applying CICD best practices

  • Excellent breath and depth in ML fundamentals

  • Experience in at least one of the following topics: Recommendation Systems & Ranking, NLP or Reinforcement learning

  • Experience with cloud based ML pipelines on GCP or AWS

  • Experience launching end-to-end production ML models

  • Experience in automating internal business processes via data and ML solutions 

  • Experience in stakeholder management and cross-functional collaboration

  • The ability to clearly communicate complex concepts to technical and non-technical audiences

  • A strong desire to perform and grow in your role


Even better:

  • A graduate degree in a technical field relevant to ML (e.g., Computer Science or other STEM fields)

  • Experience with designing and evaluating A/B tests for newly launched models

  • Experience building ML solutions for user facing teams

Additional Information

We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. 

We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square? Check out our I+D page

Additionally, we consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.


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

Block, Inc. (NYSE: SQ) is a global technology company with a focus on financial services. Made up of Square, Cash App, Spiral, TIDAL, and TBD, we build tools to help more people access the economy. Square helps sellers run and grow their businesses with its integrated ecosystem of commerce solutions, business software, and banking services. With Cash App, anyone can easily send, spend, or invest their money in stocks or Bitcoin. Spiral (formerly Square Crypto) builds and funds free, open-source Bitcoin projects. Artists use TIDAL to help them succeed as entrepreneurs and connect more deeply with fans. TBD is building an open developer platform to make it easier to access Bitcoin and other blockchain technologies without having to go through an institution.

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