Senior Deep Learning Research Engineer, Risk Labs

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level.The Risk team at Block continues this legacy through a sophisticated, technology and science-led approach to protecting our customers and their funds. Our interdisciplinary structure combines Product Development, Science teams (specializing in modeling, analytics, and data science), Operations and key partners including Legal, Compliance and Policy, all working in concert to identify, assess, and solve complex risk challenges across access, fraud prevention and compliance.This role is part of our Risk Labs team - this team’s mission is aimed at developing advanced deep learning and reinforcement learning based signals and learned representations for machine learning models. In this role, you’ll explore, develop and implement alternatives to traditional feature based ML with state-of-the-art models for solving risk problems.What You'll Be Up To:Reporting to the lead of the Risk Labs Modeling teamDreaming up, designing, and running big experiments to quickly test out cool (and practical) ideasApplying the latest theoretical breakthroughs to make our products even betterBuilding experiments, prototypes, and architectural designs across computer science fields like ML, data mining, NLP, and performance analysisContributing to the growth of new initiatives by sharing emerging trends and best practices, fostering a collaborative and innovative environmentTaking the lead on defining data structures, frameworks, designs, and evaluation metrics for your research solutions – you'll have a lot of autonomy hereTeaming up with folks inside and outside Block to find new research areas and shape our long-term strategiesRunning experiments for your research, using simulations and prototypes to see what happensRefining how we do research, solidifying your hypotheses, and helping evolve our practices with minimal hand-holdingWe’re Targeting:A Level 6 hire, which usually means something like: BSc with 7-10 years experience, MSc with 5-8 years experience, PhD with 3-5 years experienceSomeone who's been through the whole ML cycle, with a big plus for experience on the MLE/deployment sideProven experience in conducting applied research projects with tangible outcomesSome intellectual property under your belt - these contributions could take the form of open source software, patents, or refereed publications in venues such as conferences, journals, books, etc.Experience with AI, deep learning, or reinforcement learning is key, and bonus points if you've done unique things with LLMsExcellent problem-solving abilities and a creative mindset to tackle complex challengesA great communicator - someone who can easily chat with different cross-functional teams and contribute to the research communityTechnologies We Use and Teach:Python (NumPy, Pandas, sklearn, XGBoost, etc)TensorFlow, Pytorch, keras, etcMySQL, Snowflake, GCP/AWS and SparkWe'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 an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class.Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page.Block will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and 'fair chance' ordinances.Zone A: $202,600—$304,000 CADZone B: $202,600—$304,000 CADUse of AI in Our Hiring Process: We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. #J-18808-Ljbffr