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Remote Principal Machine Learning Engineer (Mexico)GrabJobs • New York, New York, United States
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Remote Principal Machine Learning Engineer (Mexico)

GrabJobs
  • US
    New York, New York, United States
  • US
    New York, New York, United States
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The salary range for this role is $50,000 - $120,000 per year (Gross in USD) About Sezzle: With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting-edge tech with seamless, interest-free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values. As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game-changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping! About the Role: We are seeking a talented and motivated
Principal Machine Learning Engineer who is best in class with a high IQ plus a high EQ. This role presents an exciting opportunity to thrive in a dynamic, fast-paced environment within a rapidly growing team, with abundant prospects for career advancement. You will be responsible for overseeing the design, development, and deployment of machine learning models that power and enhance our financial platform. In this role, you will drive the creation of scalable machine learning solutions for personalized recommendations in the Sezzle marketplace, fraud detection, and credit risk assessment, utilizing a combination of cloud services, open-source tools, and proprietary algorithms. Your leadership will be key in blending machine learning development and operations (MLOps) to automate and optimize the full lifecycle of our ML models. You will collaborate with a team of engineers and data scientists to build large-scale, high-quality solutions that address diverse challenges in the shopping and fintech space. You’ll ensure our AI-driven features are robust, efficient, and scalable as we continue to grow. Responsibilities: Design, Build, and Maintain Scalable ML Infrastructure: Lead the design and development of scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker for efficient model training and deployment. Collaborate with Product Teams: Work closely with product teams to develop MVPs for AI-driven features, ensuring quick iterations and market testing to refine solutions effectively. Develop Monitoring & Alerting Frameworks: Create and enhance monitoring and alerting systems for machine learning models to ensure high performance, reliability, and minimal downtime. Support Cross-Departmental AI Utilization: Enable various departments within the organization to leverage AI/ML models, including cutting-edge Generative AI solutions, for different use cases. Provide Production Support: Offer expertise in debugging and resolving issues related to machine learning models in production, participating in on-call rotations for operational troubleshooting and incident resolution. Scale ML Architecture: Design and scale machine learning architecture to support rapid user growth, leveraging deep knowledge of AWS and ML best practices to ensure robustness and efficiency. Mentor and Elevate Team Skills: Conduct code reviews, mentor team members, and elevate overall team capabilities through knowledge sharing and collaboration. Stay Ahead of the Curve: Stay updated with the latest advancements in machine learning technologies and AWS services, driving the adoption of cutting-edge solutions to maintain a competitive edge. Minimum Requirements: Bachelors degree in Computer Science, Computer Engineering, Machine Learning, Statistics, Physics, or a relevant technical field, or equivalent practical experience. At least 6+ years of experience in machine learning engineering, with demonstrated success in deploying scalable ML models in a production environment. Ideal Skills & Experience: Deep expertise in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or related technical fields. Proven track record of developing machine learning models from inception to business impact, demonstrating the ability to solve complex challenges with innovative solutions. Proficiency with Python is required, and experience with Golang is a plus. Demonstrated technical leadership in guiding teams, owning end-to-end projects, and setting the technical direction to achieve project goals efficiently. Experience working with relational databases, data warehouses, and using SQL to explore them. Strong familiarity with AWS cloud services, especially in deploying and managing machine learning solutions and scaling them in a cost-effective manner. Knowledgeable in Kubernetes, Docker, and CI/CD pipelines for efficient deployment and management of ML models. Comfortable with monitoring and observability tools tailored for machine learning models (e.g., Prometheus, Grafana, AWS CloudWatch) and experienced in developing recommender systems or enhancing user experiences through personalized recommendations. Solid foundation in data processing and pipeline frameworks (e.g., Apache Spark, Kafka) for handling real-time data streams. About You: You have relentlessly high standards - many people may think your standards are unreasonably high. You are continually raising the bar and driving those around you to deliver great results. You make sure that defects do not get sent down the line and that problems are fixed so they stay fixed. You’re not bound by convention - your success—and much of the fun—lies in developing new ways to do things You need action - speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking. You earn trust - you listen attentively, speak candidly, and treat others respectfully. What Makes Working at Sezzle Awesome: At Sezzle, we are more than just brilliant engineers, passionate data enthusiasts, out-of-the-box thinkers, and determined innovators. We believe in surrounding ourselves with only the best and the brightest individuals. Our culture is not defined by a certain set of perks designed to give the illusion of the traditional startup culture, but rather, it is the visible example living in every employee that we hire. Li-Remote
  • New York, New York, United States

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