Machine Learning Engineer

• ML Infrastructure and Operations: Design, build and maintain the infrastructure required for optimal extraction, transformation, and loading of data from various sources. Develop and manage data pipelines and workflows for machine learning models.
• Model Development and Deployment: Design, develop, and implement machine learning models for underwriting and other financial service applications. Ensure models are robust, scalable, and maintainable.
• Collaboration: Work closely with data scientists, software engineers, and product managers to integrate machine learning models into production systems. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Performance Monitoring: Monitor and evaluate the performance of deployed models, ensuring they meet the desired accuracy and efficiency metrics. Implement processes for continuous improvement and optimization of models.
• A/B Testing and Experimentation: Design and implement experiments to optimize models and ensure they align with business goals.
• Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of learning and growth within the team.

This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products. We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships. Yes, you can build an exciting business AND have real-life real-customer impact.

We are seeking a Senior Machine Learning Engineer to join our team. This role will focus on developing and maintaining machine learning infrastructure and operations, particularly for our cash advance underwriting model and other machine learning use cases. The ideal candidate will have a strong background in software development, machine learning, and data engineering, with experience in deploying scalable ML models in production environments., * Educational Background: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree preferred.

• Experience: Minimum of 3 years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments.
• Technical Skills:
• Proficiency in programming languages such as Python or Ruby.
• Strong understanding of data structures, algorithms, and software design principles.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch).
• Familiarity with MLOps practices and tools for continuous integration and deployment of ML models.
• Experience with cloud services (e.g., AWS, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Analytical Skills: Strong problem-solving skills with the ability to analyze complex data sets, apply advanced data science techniques, and derive actionable insights. Proficient in building predictive models, performing statistical analysis, and utilizing machine learning algorithms to identify trends, patterns, and opportunities for optimization.
• Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.