Principal Engineer, ML/AI Platform

Credit Acceptance is proud to be an award-winning company with local and national workplace recognition in multiple categories! Our world-class culture is shaped by dedicated Team Members who share a drive to succeed as professionals and together as a company. A great product, amazing people and our stable financial history have made us one of the largest used car finance companies nationally.

Our Engineering and Analytics Team Members utilize the latest technology to develop, monitor, and maintain complex practices that help optimize our success.  Our Team Members value being challenged, are encouraged to express their ideas, and have the flexibility to enjoy work life balance.  We build intrinsic value by partnering with all functions of our business to support their success and make strategic business decisions.  We focus on professional development and continuous improvement while enjoying a casual work environment and Great Place to Work culture!

The ML/AI Platform team at Credit Acceptance (CA) designs builds and maintains end-to-end ML/AI platforms to support and automate the lifecycle of the machine learning and Gen AI workflows, including standardization of dev environment, auto-labeling, feature stores and experiment management, model development, debugging and evaluation, containerization, deployment, and monitoring. The team also manages all the internal and external tools integrated into the platform for seamless ML/AI operations. Besides managing the central ML and Gen AI platform, the team is also responsible for designing and building custom large-scale and efficient end-to-end solutions that can be seamlessly integrated with other systems and processes.

As a leader within the team, you will help democratize ML/AI and its applications for Credit Acceptance, ensuring it remains easily accessible, robust, trustable, scalable, and cost-efficient. You will meet our internal customers where they are and optimize towards their needs by delivering value incrementally and coupled to their problems. Being part of a small yet impactful team means having a broad scope of responsibility, and as ML is still in its early stages, this role provides a chance to grow as a leader by mentoring others across the company. This is an exciting opportunity to own and help define the future of machine learning within a rapidly growing team!

Outcomes and Activities:

• This position will work from home; occasional planned travel to an assigned Southfield, Michigan office location may be required.  However, this position is permitted to work at a Southfield, Michigan office location if requested by the team member
• Develop, maintain, and enhance reusable frameworks for AI/ML model development and deployment while establishing and driving best practices in machine learning engineering and Operations.
• Partner with the Cloud Engineering team in the strategic execution, including the road mapping and the technical designs for the feature, training, serving infrastructure, and underlying operational infrastructure that provides incremental delivery and impact.  
• Design, advocate, and implement cutting edge technologies for availability, scalability, operational excellence, and cost management while delivering incrementally.
• Collaborate closely with the ML and Tech Delivery Engineers, and Business Ops and Product Managers to understand their needs and identify opportunities to improve the efficiency of the AI/ML process.
• Closely follow industry and academic developments in the SOTA for the AI lifecycle and ML Systems domain’s and adopt technology that is the best fit for business and industry needs
• Partner, mentor and/or educate ML and Data Engineers on current and up and coming tools and technologies for ML operations through presentations and documentation.
• Help design and architect an AI platform that adheres to the principles of responsible AI and simplifies privacy compliance.
• Utilize your deep understanding of data characteristics, model architectures, optimization techniques, or other ML domain-specific challenges to perform critical analysis of modeling results
• Lead build vs buy discussions on technologies that would underpin the feature, training, and serving layers.
• Conduct technical interviews with well-calibrated standards and play an essential role in recruiting activities. 
• Effectively onboard and mentor junior engineers and/or interns

Competencies: The following items detail how you will be successful in this role.

• Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer’s shoes, comprehending their needs and challenges, and using that understanding t