Since we opened our doors in 2009, the world of commerce has evolved immensely, and so has Square. After enabling anyone to take payments and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldn’t work together.
To solve this problem, we expanded into software and built integrated solutions to help sellers sell online, manage inventory, book appointments, engage loyal buyers, and hire and pay staff. 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 in one place. Afterpay furthers our goal to provide omnichannel tools that unlock meaningful value and growth, enabling sellers to capture the next generation shopper, increase order sizes, and compete at a larger scale.
Today, we are a partner to sellers of all sizes – large, enterprise-scale businesses with complex operations, sellers just starting, as well as merchants who began selling with Square and have grown larger over time. As our sellers grow, so do our solutions. There is a massive opportunity in front of us. We’re building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.
As a Machine Learning Engineer within the Risk Machine Learning and Decision Science team, you work on projects that enable a software driven, machine learning centric view on all money movement and every transaction within the rapidly growing Square ecosystem. This touches on actively maximizing the trade off of revenue growth and risk using artificial intelligence. The machine learning driven software that we release interacts with every transaction and money movement within our seller ecosystem - a profound degree of scale and impact. Such machine learning techniques touch on reinforcement learning, decision theory, deep learning sequence modeling, natural language processing, and optimization theory. In addition, we also strive to provide our sellers, through seller facing products, with transparency around why our machine learning made a particular decision. This touches on algorithms in the relatively new space of explainable artificial intelligence.
Our algorithms derive value from our unique and rich data from our entire product portfolio within our rapidly growing seller ecosystem. We partner with business, product, operations, and engineering teams to drive optimal decision making systems using sophisticated modeling and machine learning. We’re a passionate team of entrepreneurs, scientists, and engineers who are shipping machine learning software that actively actively manages Square’s view on each transaction as it pertains to our revenue growth and risk.
- Build machine learning/deep learning models that analyze payment activity in real time across our Seller’s ecosystem consisting of payments, banking, and debit card products.
- Adapt existing machine learning methods and transfer learning to develop solutions that work at global scale.
- Leverage an experimentation mindset along with state-of-the-art algorithms to create preventative systems, collaborate on new product features to drive losses down, and explore new datasets (including 3rd party data) to engineer new features for our models.
- Collaborate with business leaders, subject matter experts, and decision makers to develop success criteria and optimize new products, features, policies, and models
- Research, design, develop, and test a range of classification, regression and optimization problems
- An advanced degree (M.S., PhD.), preferably in Computer Science,Engineering, Statistics, Physics, Mathematics or a related technical field.
- PhD plus 3 years (or Master plus 5 years) industry working experience in applied Machine learning or Deep learning
- A strong track record of performing machine learning model development using Python (numpy, pandas, tensorflow, pytorch, scikit-learn, etc.) and SQL/NoSQL interaction patterns.
- Expert level knowledge of modern techniques in machine learning and deep learning, e.g., tree models, transformer network architectures, with an orientation to maximizing such algorithms in a large scale production setting.
- 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.
- The ability to clearly communicate complex results to technical and non-technical audiences and stakeholders (PMs, Operations, Engineers).
Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.
Zone A: USD $167,300 - USD $204,500
Zone B: USD $158,900 - USD $194,300
Zone C: USD $150,600 - USD $184,000
Zone D: USD $142,200 - USD $173,800
To find a location’s zone designation, please refer to this resource. If a location of interest is not listed, please speak with a recruiter for additional information.
Benefits include the following:
- Healthcare coverage
- Retirement Plans including company match
- Employee Stock Purchase Program
- Wellness programs, including access to mental health, 1:1 financial planners, and a monthly wellness allowance
- Paid parental and caregiving leave
- Paid time off
- Learning and Development resources
- Paid Life insurance, AD&D. and disability benefits
- Perks such as WFH reimbursements and free access to caregiving, legal, and discounted resources
This role is also eligible to participate in Block's equity plan subject to the terms of the applicable plans and policies, and may be eligible for a sign-on bonus. Sales roles may be eligible to participate in a commission plan subject to the terms of the applicable plans and policies. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
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.
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.