About the job
Join kikoff as a Senior Machine Learning Engineer in our vibrant San Francisco office, where you'll play a pivotal role in enhancing our machine learning infrastructure and operations. Your primary focus will be on developing and maintaining our cash advance underwriting model and other innovative machine learning applications. The ideal candidate will bring a robust background in software development, machine learning, and data engineering, coupled with hands-on experience deploying scalable ML models in production settings.
Key Responsibilities:
ML Infrastructure and Operations: Design, construct, and oversee the infrastructure necessary for the efficient extraction, transformation, and loading of data from diverse sources. Develop and maintain data pipelines and workflows tailored for machine learning models.
Model Development and Deployment: Create, develop, and execute machine learning models for underwriting and various financial service applications, ensuring they are robust, scalable, and maintainable.
Collaboration: Collaborate closely with data scientists, software engineers, and product managers to seamlessly integrate machine learning models into production systems. Work with cross-functional teams to comprehend business requirements and translate them into effective technical solutions.
Performance Monitoring: Continuously monitor and assess the performance of deployed models, ensuring they achieve the desired accuracy and efficiency metrics. Implement processes for ongoing improvement and optimization of models.
A/B Testing and Experimentation: Design and execute experiments aimed at optimizing models to align with business objectives.
Mentorship: Provide mentorship and guidance to junior engineers, cultivating a culture of continuous learning and growth within the team.
Qualifications:
Educational Background: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field; an advanced degree is preferred.
Experience: At least 3 years of experience in machine learning engineering with a proven record of deploying ML models in production environments.
Technical Skills:
Expertise in programming languages, particularly Python or Ruby.
Solid understanding of data structures, algorithms, and software design principles.
Hands-on experience with machine learning frameworks and tools.