November 23
• The salary range for this role is $50,000 - $120,000 per year (Gross in USD). • 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. • We are seeking a talented and motivated Principal Machine Learning Engineer who is best in class with a high IQ plus a high EQ. • You will oversee the design, development, and deployment of machine learning models that power and enhance our financial platform. • 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.
• Bachelor's 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. • 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.
Apply NowNovember 5
Develop machine learning models to combat fraud for a fintech.