Machine Learning Engineer (Modeling) - Support AI Tooling

Key Skills

Deep Learning, Google Cloud Platform (GCP), Java Programming, Keras software library, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), NumPy, Pandas, Python Programming, PyTorch, TensorFlow

Job Description

Machine Learning is an integral part of how we design products, operate, and pursue Cash App’s mission to serve the unbanked as well as disrupt traditional financial institutions. Our massive scale and deep trove of transaction data create an endless number of opportunities to use artificial intelligence to better understand our customers and offer new products and experiences that can improve their lives. We are a highly creative group that prefers to solve problems from first principles; we move quickly, make incremental changes, and deploy to production every day. Check out our Machine Learning / AI blog here!

This role is part of our Cash App's ML team and will be deeply embedded within one of our product teams - here are the workstreams we're currently hiring for:

Support Support AI Tooling

We aim to create better-than-human Customer Support Experiences by providing our support advocates real-time intelligent assistant empowered by Gen AI models. Cash App holds people’s money, and building customers’ trust is absolutely essential to our brand. Our team builds this trust by providing fast and reliable help whenever the customer needs through our advocates.

You will:

• Experiment with state-of-the-art algorithms to improve knowledge retrieval and search efficiency
• Develop Natural Language Processing and Gen AI models to improve effectiveness of customer support
• Use large language models to suggest responding messages to customer support advocates in real time
• Create data features and embeddings from various data structures, (text/non-text, bath/streaming)
• Collaborate with cross-functional teams to understand business needs and develop impactful solutions
• Create both batch and realtime models training and deployment designs
• Continuously monitor and evaluate the performance of machine learning models, making adjustments as necessary
• Prepare and present reports on model performance and trends to stakeholders

Qualifications

You have:

• Must be based in Pacific OR Mountain Time Zone
• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. A Master's degree is preferred
• 5+ years of experience in machine learning, data analysis, or a related field
• Proven experience in Deep Learning, Natural Language Processing, Large Language Models, Search & Recommendation
• Strong knowledge of machine learning algorithms and data analysis techniques
• Proficiency in programming languages such as Python or Java
• Excellent problem-solving skills and attention to detail
• Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders

Technologies we use and teach:

• Python (NumPy, Pandas, sklearn, TensorFlow, PyTorch, keras, etc.)
• Snowflake, DataBricks, GCP, AWS
• Classical classification / regression models

Additional Information

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 $163,600 - USD $245,400

Zone B: USD $155,400 - USD $233,200

Zone C: USD $147,300 - USD $220,900

Zone D: USD $139,000 - USD $208,600

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.

Full-time employee benefits include the following:

• Healthcare coverage (Medical, Vision and Dental insurance)
• Health Savings Account and Flexible Spending Account
• 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 (including 12 paid holidays)
• Paid sick leave (1 hour per 26 hours worked (max 80 hours per calendar year to the extent legally permissible) for non-exempt employees and covered by our Flexible Time Off policy for exempt employees)
• Learning and Development resources
• Paid Life insurance, AD&D, and disability benefits

These benefits are further detailed in Block's policies. 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.