Description:

Sezzle is a leading financial technology company focused on empowering consumers with flexible payment options and innovative shopping experiences.
The Principal Machine Learning Engineer will join the core AI/ML team to oversee the design, development, and deployment of machine learning models for Sezzle's financial platform.
This role involves creating scalable machine learning solutions for personalized recommendations, fraud detection, and credit risk assessment using cloud services, open-source tools, and proprietary algorithms.
The engineer will blend machine learning development and operations (MLOps) to automate and optimize the full lifecycle of ML models.
Responsibilities include designing scalable ML infrastructure on AWS, collaborating with product teams, developing monitoring and alerting frameworks, supporting cross-departmental AI utilization, providing production support, scaling ML architecture, mentoring team members, and staying updated with advancements in machine learning technologies.

Requirements:

A Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, Statistics, Physics, or a relevant technical field, or equivalent practical experience is required.
At least 6+ years of experience in machine learning engineering with a proven track record of deploying scalable ML models in a production environment is necessary.
Deep expertise in machine learning, recommendation systems, pattern recognition, data mining, or related fields is preferred.
Proficiency in Python is required, and experience with Golang is a plus.
Demonstrated technical leadership in guiding teams and owning end-to-end projects is essential.
Experience with relational databases, data warehouses, and SQL is required.
Strong familiarity with AWS cloud services for deploying and managing machine learning solutions is necessary.
Knowledge of Kubernetes, Docker, and CI/CD pipelines for ML model management is preferred.
Experience with monitoring tools for machine learning models and developing recommender systems is required.
A solid foundation in data processing and pipeline frameworks for handling real-time data streams is necessary.

Sezzle offers a dynamic work environment that values high standards and encourages innovation.
The company promotes a culture of trust, respect, and collaboration among its employees.
Employees are empowered to take calculated risks and make decisions swiftly.
Sezzle is committed to diversity and inclusion, ensuring an equitable workplace for all employees.
The company provides opportunities for professional growth and skill development through mentorship and knowledge sharing.