About the role
<div class="content-intro"><p>Flex is a growth-stage, NYC headquartered FinTech company that is creating the best rent payment experience. It’s hard to believe that it’s 2026 and paying rent on time is expensive, inflexible, and difficult. We’re here to change that! Flex enables our users to pay rent throughout the month on a schedule that better fits their finances and budget. Our mission is to empower as many renters as possible with flexibility over their most significant recurring expense. After deliberately keeping a stealth profile as we built up unprecedented investor support and an enthusiastic user base, we are looking for motivated individuals to help us keep our mission growing. Will you be a part of the team?</p></div><h3><strong>About the role</strong></h3> <p>We are seeking an experienced Senior Staff Machine Learning Engineer to join our dynamic team and take a leading role in developing cutting-edge machine learning systems that drive business growth. As a key technical contributor, you will drive the development, deployment, and scalability of machine learning models in a production environment, ensuring they deliver value and performance at scale. You will collaborate closely with data scientists, product teams and engineers to implement state-of-the-art solutions that power our products and services through continuous innovation.</p> <h3><strong>What you’ll do</strong></h3> <ul> <li>Own the end-to-end lifecycle of machine learning projects, from data collection and preprocessing to model deployment, monitoring, and maintenance in a production environment.</li> <li>Build, maintain, and optimize robust data pipelines that support model development, training, and deployment at scale.</li> <li>Implement machine learning algorithms and models that meet performance, scalability, and reliability requirements in a production setting.</li> <li>Collaborate with data scientists, engineers, and product teams to design and deploy machine learning systems that address business and product needs.</li> <li>Continuously monitor and improve model performance, conducting experiments, tuning hyperparameters, and ensuring models meet business objectives.</li> <li>Leverage distributed computing frameworks and cloud-based platforms to process large-scale datasets efficiently.</li> <li>Stay up-to-date with the latest advancements in machine learning, software engineering practices, and deployment strategies to keep our systems cutting-edge.</li> <li>Candidates with domain expertise in areas like payment risk, fraud detection, or customer success are highly preferred.</li> <li>Expertise and familiarity with NLP models are considered an asset.</li> </ul> <h3><strong>Key qualifications</strong></h3> <ul> <li>