Block builds technology to increase access to the global economy. Across our ecosystem, including Square and Cash App, we create tools that help businesses run and grow, and individuals move, manage, and grow their money with confidence. Square empowers sellers of all sizes with integrated, omnichannel tools to accept payments, manage operations, access financial services, and reach customers across online and in-person channels. Cash App complements this by providing a fast, accessible financial platform for millions of people to send, spend, save, invest, and borrow, helping redefine how individuals interact with money.Operating at massive scale across both ecosystems means trust and safety are foundational. Our teams build systems that protect real people and businesses, safeguard financial activity, and ensure our products remain reliable, secure, and easy to use. Block is a global, distributed company with a culture rooted in ownership, creativity, and impact. Whether supporting sellers on Square or customers on Cash App, we're united by a shared mission: to make the global economy more accessible and inclusive. The Role We're hiring Senior Machine Learning Engineers to join Block's Risk Machine Learning organization, where teams apply ML at massive scale to detect, prevent, and reduce fraud and abuse across Cash App and Square. This opening supports multiple senior-level roles, with team placement determined through a collaborative matching process based on your experience, interests, and current business needs. Today, we're growing teams focused on chargeback and fraud prevention as well as trust and safety initiatives supporting Cash App Families and teen banking. Across teams, your work will directly protect our ecosystem, reduce financial loss, and enable safe, seamless financial experiences for millions of customers, sellers, and families. Team Focus Areas Depending on your background and interests, you may join a team focused on one of the following areas: Fraud & Chargeback Risk Build machine learning models and systems that detect fraudulent transactions, reduce chargebacks, and protect the integrity of payments across Cash App and Square. Trust, Safety & Families Develop machine learning systems that enable safe and trustworthy financial experiences for teens and families by detecting risky or abusive behavior, strengthening account integrity, and supporting long-term trust in Cash App. Both teams value first-principles thinking, rapid iteration, and shipping production ML solutions with real-world impact. You Will Partner with product, engineering, data science, policy, and operations to design and productionize ML-driven risk solutions at scale Own end-to-end machine learning systems, from problem definition and modeling to deployment, monitoring, and iteration Lead technical decision-making within your workstreams and influence ML strategy and planning Build tooling and processes that improve the speed, reliability, and impact of the ML development lifecycle Apply state-of-the-art modeling techniques and third-party data sources to improve detection and decision-making Investigate emerging fraud, abuse, and risk patterns to proactively inform product safeguards and policy Collaborate closely with ML platform and engineering teams to ensure models operate reliably in real time and at scale You Have 2+ years of industry experience in machine learning, applied AI, or related fields Bachelor's degree in a quantitative field (Computer Science, Engineering, Statistics, Physics, Applied Math); Master's or PhD preferred Proven experience independently designing, deploying, and maintaining ML solutions in production Strong familiarity with techniques such as tree-based models, deep learning, transfer learning, or reinforcement learning Experience influencing technical direction and collaborating with cross-functional partners at scale Strong communication skills, sound judgment, and an ownership mindset Curiosity and alignment with Block's mission of economic empowerment Technologies We Use and Teach Python (NumPy, Pandas, scikit-learn, XGBoost, PyTorch, TensorFlow/Keras) PySpark, MLflow, workflow orchestration tools (Airflow, Prefect) GCP (Vertex AI), AWS Snowflake, MySQL, Tableau, Mode Containerization, CI/CD, and production ML best practices 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.

Libertex Group Overview Established in 1997, the Libertex Group has helped shape the online trading industry by merging innovative technology, market movements and digital trends. The multi-awarded online trading platform, Libertex, enables traders to access the market and invest in stocks or trade CFDs with underlying assets being commodities, Forex, ETFs, cryptocurrencies, and others. A firm believer in the power of sports to inspire, empower and push for success, Libertex is the Official Online Trading Partner of KICK Sauber F1 Team. We build innovative fintech so people can #TradeForMore with Libertex. Job Overview We are looking for a Senior Machine Learning Engineer who will play a pivotal role in implementing, enhancing, and maintaining our machine learning solutions. Responsibilities: Work closely with business departments (Sales, Marketing, Products, Payments, Dealing) to understand their needs and translate these into practical, scalable, and robust machine learning solutions. Work on such projects like LTV prediction, churn prediction, antifraud solutions, uplift modelling, GenAI-applications Oversee current solutions, manage improvements if needed, expand the role of machine learning across organisation. Implement new and improve current MLOps practices, laying the foundation for efficient and effective model development, deployment, and monitoring. Implement new and support existing LLM projects (chatbots, knowledge base etc). Requirements Benefits Quarterly bonuses based on Company performance Generous relocation package for the employee and their immediate family/partner Medical Insurance Plan with coverage for the employee and their immediate family from day one 24 working days of annual leave Yearly reimbursement of travel expenses for the employee and family's flight home Corporate events and team building activities Udemy Business unlimited membership & language training courses Professional and personal development opportunities in a fast-growing environment Libertex Group is an equal opportunity employer, fostering an inclusive and diverse environment. We do not discriminate based on any characteristic protected by the law. Candidate privacy is respected, and all data is securely stored and used solely for recruitment purposes, in line with GDPR and our internal policies. Unsuccessful applicants may have their data retained for future opportunities unless deletion is requested.

• Model Development & Deployment: Design and optimize LLM-based models, algorithms, and agents, then deploy them into production environments with a focus on scalability, reliability, and performance. • LLM & Multi-Agent Systems: Develop and maintain advanced LLM-powered systems and multi-agent architectures to automate and accelerate cybersecurity risk assessment workflows. This includes designing conversational AI agents, orchestrating interactions between multiple agents, and building and integrating scalable RESTful APIs and microservices to expose model capabilities for integration with broader product ecosystems. • Performance Monitoring: Supporting observability and evaluation of LLM-based agents to ensure long-term model accuracy, robustness, and stability. • Data Pipeline Creation: Build and maintain scalable data pipelines to preprocess, clean, and transform raw data for analysis and model training. • Research and Experimentation: Stay updated on the latest agent, LLM, and ML techniques, tools, and frameworks to enhance model accuracy and efficiency. • Collaboration: Work closely with data scientists, ML engineers, software engineers, and product teams to understand requirements and integrate ML solutions into products. • Documentation: Create clear and concise documentation for models, processes, and systems to support team collaboration and knowledge sharing.

• Analyze and validate product team hypotheses by interpreting large-scale datasets, identifying trends, and generating actionable insights. • Conduct external data analysis by integrating and assessing new data sources, replacing legacy sources, and ensuring compliance with terms of service. • Extract and create features from datasets for machine learning models, driving predictive capabilities and enhancing product performance. • Collaborate with cross-functional teams to translate data-driven insights into business recommendations that guide product decisions and strategy. • Apply advanced statistical analysis, linear algebra, and modeling techniques to large datasets, providing deep, actionable insights. • Perform large-scale data analysis comfortably using tools such as Python and SQL. • Work with Azure Machine Learning for model training and deployment, leveraging cloud capabilities to enhance data analysis workflows. • Participate in Scrum agile development process, focusing on iterative improvements through data-informed decision-making.