Senior Machine Learning Engineer, Financial Crimes
3 weeks ago
Company Description
It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 47 million monthly active customers. We want to redefine the world's relationship with money to make it more relatable, instantly available, and universally accessible.
Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We've been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.
Check out our locations, benefits, and more at
Job Description
The Financial Crimes Technology team at Cash App detects and reports illegal and suspicious activity on Cash App. We work globally with partners in Product, Counsel and Engineering to ensure we are providing a safe user experience for our customers while minimizing or eliminating bad activity on our platform.
We use Machine Learning and Generative AI as an important part of our toolkit. As Cash App scales, we monitor hundreds of billions of dollars in transactions across traditional payment and blockchain networks. Our machine learning systems monitor and surface suspicious activity (money laundering, illegal activity and terms of service violations) for agent review. Our systems block payments in real-time where appropriate. We use generative AI technologies to improve agent workflow and case review tools, by adding features that accelerate agent productivity and allow them to make more informed and accurate decisions. We are looking for a senior MLE that can integrate vertically into the ML sub-team and focus on building/enhancing tools, libraries, frameworks, developer environments etc. for ML modeling workflows.
This is an IC role reporting into the Data Science and ML Modeling Manager that has leadership responsibilities including driving strategic roadmaps and priorities to completion by collaborating with cross functional stakeholders.
You will:
- Design, build and enhance batch and real-time inference services and tooling that support our ML use cases
- Facilitate modelers on the team by unblocking access to the infrastructure/tools necessary for development including MLOps
- Develop prototypes and partner with ML modelers to encourage adoption of new tools and technologies and plan for future needs of our ML teams
- Join a new and growing team and have a significant impact on influencing team culture.
Qualifications
You have:
- 4+ years of combined Machine Learning and Engineering industry experience (full stack ML experience)
- A Bachelor's degree in computer science, data science, operations research, applied math, stats, physics, or related technical field
- Familiarity with Linux/OS X command line, version control software (git), and software development principles with a machine learning software development life-cycle orientation.
- Experience working with product, business, and engineering to prioritize, scope, design, and deploy ML models
- Familiarity with Python computing stack, MySQL, Snowflake, Airflow, Java/Go
- Hosted models for inference on public clouds like GCP, AWS and/or built micro-services to facilitate event based triggering, feature generation, model inference and downstream actioning.
Qualifications
You have:
- 4+ years of combined Machine Learning and Engineering industry experience (full stack ML experience)
- A Bachelor's degree in computer science, data science, operations research, applied math, stats, physics, or related technical field
- Familiarity with Linux/OS X command line, version control software (git), and software development principles with a machine learning software development life-cycle orientation.
- Experience working with product, business, and engineering to prioritize, scope, design, and deploy ML models
- Familiarity with Python computing stack, MySQL, Snowflake, Airflow, Java/Go
- Hosted models for inference on public clouds like GCP, AWS and/or built micro-services to facilitate event based triggering, feature generation, model inference and downstream actioning.
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