Systems Research Engineer- Software Engineer, Data and ML Infrastructure

<div class="content-intro"><p style="text-align: justify;">Feedzai is the world’s first RiskOps platform for financial risk management, and the market leader in safeguarding global commerce with today’s most advanced cloud-based risk management platform, powered by machine learning and artificial intelligence. Feedzai is securing the transition to a cashless world while enabling digital trust in every transaction and payment type. The world’s largest banks, processors, and retailers trust Feedzai to protect trillions of dollars and manage risk while improving the customer experience for everyday users, without compromising privacy. Feedzai is a Series D company and has raised $282M to date. With a valuation of $2 billion, our technology protects 1 billion consumers and 90 billion transactions each year.</p></div><p><strong>Feedzai Research</strong> focuses on anticipating and solving Feedzai’s long-term goals. We strive to develop ethical cutting edge technology to stay one step ahead of the fraudsters of tomorrow. We work closely with Product and Data Science to build distributed systems that support high-load, operate 24/7 with low latencies, and use Machine Learning to make decisions and fight fraud more efficiently. As a research team, we focus on long-term, disruptive, state-of-the-art research, produce and protect our IP, publish peer reviewed work, contribute to open-source, and partner with external researchers and universities.</p> <p>We are looking for someone that combines technical knowledge and research skills, is passionate about building cloud native applications that scale to process millions of events per second, while providing low-latency and fault-tolerance guarantees.</p> <p><strong><em>Come and change the world with us.</em></strong></p> <p>&nbsp;</p> <p><strong>Your Day to Day:</strong></p> <ul> <li>Design and implement real-time Machine Learning inference systems able to process millions of events per second while achieving low-latency and fault tolerance guarantees.</li> <li>Define technical standards and best practices for new cloud-native services, with particular focus on Data and ML Infrastructure.</li> <li>Drive architectural decisions for Machine Learning systems workloads, balancing performance, cost, reliability, and user experience.</li> <li>Be hands-on, executing the full software development life cycle and writing well-designed and testable code.</li> <li>Design and develop Communicate findings and results to internal and external stakeholders, including writing scientific papers and patents.</li> <li>Review and evaluate state-of-the-art research work on topics including Data Processing, Machine Learning Systems, Graphs, etc.</li> <li>Own services throughout their lifecycle f