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 50+ 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.The RoleThe Risk AI team's mission is to develop cutting-edge deep learning-based signals and learned representations for Risk ML models. Our focus is on exploring, developing, and implementing state-of-the-art (SOTA) alternatives to traditional feature-based machine learning methods. By leveraging the latest advancements in AI, we aim to enhance real-time evaluation pipelines used by the Risk ML team to detect and prevent fraudulent transactions and activities. Cash App holds people's money, and maintaining customers' trust is absolutely essential to our brand. Preventing scams and fraud is a major component of building that trust. The Risk AI team is at the forefront of this effort, utilizing SOTA approaches to drive innovation and protect our users from emerging threats.You will:Design, build and maintain Machine Learning systems that enable us to flag fraudulent activities in real-timeWork hand-in-hand with ML Modellers to identify and integrate new data sources, heuristics and modelsSolve challenging technical problems at scale, collaborating with colleagues located across the globeOwn your solutions from design through to operation: we all share the pager!Lead the conception, design, and implementation of large-scale experiments to validate novel ideas rapidly and comprehensivelyApply the latest theoretical advancements to enhance existing products, processes, and technologies, ensuring they remain at the forefront of the industryEngage in the creation of experiments, prototyping, and architectural design, contributing to a diverse range of computer science domains such as machine learning, data mining, natural language processing, and performance analysisTake the lead in designing systems, data structures, frameworks, and evaluation metrics for research solution developmentConduct experiments aligned with research inquiries, employing simulations and prototypes to assess the outcomesYou have:~8 years of Software Engineering + some professional ML experienceA track record of solving business problems with technology and proven experience successfully taking ownership of an end-to-end solutionHave lead complex, multi person projectsWork autonomously in a fast paced, ambiguous and unpredictable environmentBe naturally curious & eager to learnWork creatively, taking initiative and leading when requiredGrow your solutions, maintaining and fixing them as necessaryCommunicate via clear and concise writing to facilitate asynchronous collaboration across multiple time zonesReason about complex, distributed systems at high scaleProficiency in machine learning techniques, experimental design and data engineeringStrong programming skills in languages such as Python, TensorFlow, or PyTorch.Technologies We Use and TeachPython, Java, KotlinOnnx/PyTorch, LightGBM/XGBoost
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