Machine Learning Engineer, Senior Manager

Overview

Credit Acceptance is an award-winning company recognized locally and nationally across multiple categories. We are a large, stable used car finance company with a collaborative culture. Our Engineering and Analytics teams develop, monitor, and maintain complex practices to optimize success. We value professional development, continuous improvement, work-life balance, and a casual environment aligned with our Great Place to Work culture.

We are seeking a highly motivated and experienced Leader of ML and AI Engineering within the AI team. The ideal candidate will have a strong technical background in decision science, machine learning, and generative AI with a proven track record in solving business problems and implementing large-scale automated solutions in partnership with engineering teams. The leader will partner with business and engineering stakeholders to formulate the vision to achieve the company’s strategic goals and co-lead the roadmap to deliver innovative solutions for dealers, consumers, and team members.

As a Senior Manager, MLE at Credit Acceptance, you will lead the development of AI-powered solutions across different business areas, understand business processes, identify opportunities to add value with ML/AI, and leverage data sources to build state-of-the-art ML/AI solutions.

• Lead the vision and strategic execution with a strong focus on continuous and long-term value creation across all participants of our flywheel
• Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans
• Drive execution and delivery of ML/AI solutions by prioritizing work, managing deadlines, and delivering high-quality results
• Design and deliver scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices; foster a culture of building high-quality, well-tested systems
• Troubleshoot and resolve complex technical issues to improve reliability, scalability, and operational efficiency
• Ensure security, scalability, and architectural integrity of feature designs through cross-team reviews
• Deliver hands-on solutions while mentoring other data professionals within the organization
• Explore and apply advanced ML techniques, including large language models, deep learning, and graph neural networks
• Guide a team of MLEs across different areas with mentoring, growth, Gen-AI, lifecycle management, and collaboration with engineering partners to build ML and Gen-AI platforms and inference pipelines

• Customer Empathy: Understanding customer perspectives and needs to provide a better, customer-centric experience
• Engineering Excellence: Craftsmanship, best practices, innovation, and high standards in solutions
• One Team: Collaborative, boundary-free teamwork with shared goals
• Owner’s Mindset: Responsibility, accountability, strategic thinking, and proactive domain management

• Hands-on expertise in scaling and maintaining production-grade ML services with a focus on ML/LLM Operations (versioning, automation, observability, automated training and monitoring)
• Passion for identifying new business opportunities and experience with a test-and-learn approach to scalable AI/ML solutions
• Experience partnering with engineering, product, business operations, legal, and other teams to design, build, and execute solutions
• Strong problem-solving skills with bias for action

• Experience in the automotive industry, especially in building ML/AI systems with regulatory considerations
• Experience in model interpretability and responsible AI practices
• Expertise in data science, experimentation and visualization techniques
• Experience with pipelines using DAGs (e.g., Kubeflow, DVC, Ray)
• Ability to build batch and streaming microservices exposed via gRPC or GraphQL
• Experience with Databricks MLflow for ML lifecycle management and model versioning
• Hands-on experience with Databricks Model Serving for production deployments
• Proficiency with GenAI frameworks/tools and technologies such as Apache Airflow, Spark, Flink, Kafka/Kinesis, Snowflake, and Databricks
• Demonstrable experience in parameter-efficient fine-tuning,…