Staff Machine Learning Engineer

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Credit Acceptance is proud to be an award-winning company recognized both locally and nationally across multiple workplace categories. Our world-class culture is shaped by dedicated team members who are driven to succeed as professionals individually and together as a team. Backed by a strong product, exceptional people, and a stable financial foundation, we've grown into a leading provider of used and new car financing across the country. 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! We are seeking a highly motivated and experienced Staff MLE within 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 the respective engineering teams. In this role, you 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 Staff, MLE at Credit Acceptance, you will play a pivotal role in the success of this mission as you would lead the development of AI-powered solutions across different business areas. This involves understanding the business processes, identifying new opportunities to add value using ML/AI algorithms and harnessing data sources to build state-of-the-art ML/AI solutions. 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. ML Outcomes: Explore and apply advanced machine learning techniques, including not limited to large language models (LLMs), deep learning, and graph neural networks, to solve complex challenges across the organization. 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 managing priorities, deadlines, and deliverables, leveraging your technical expertise. Design and deliver scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices, and nurture the culture of creating high-quality, well-tested systems to address critical product and business needs. Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency. Ensure the security, scalability, and architectural integrity of feature designs through reviews across teams. Deliver hands-on solutions while mentoring other data professionals (including MLEs) within the organization Guide a team of MLEs across different areas: Mentoring: Mentor team members on design principles, coding standards, and the adoption of AI productivity tools. Recommendations - Personalize guidance across different surfaces using deep learning methods; personalize layouts with Bayesian contextual multi-armed bandits Growth: Foster long-term growth through data-driven causality and incrementality Gen-AI: Power existing applications with Gen AI models and engineering to improve downstream experience and decisions Lifecycle - Using ML models (such as XGBoost & Causal Meta-Learner-based model, etc), proactively guide business teams across different areas Engineering - With engineering partners, build ML and Gen-AI platform and inference pipelines for different types of models Gen AI Outcomes: Architect and implement enterprise-grade LLM-powered solutions, managing the full lifecycle from business requirements to production deployment, monitoring, and continuous optimization Design and develop multi-agent GenAI systems using state-of-the-art frameworks (LangChain, LlamaIndex) to orchestrate complex workflows across retrieval augmentation, data operations, and compliance verification Engineer robust Retrieval Augmented Generation (RAG) pipelines incorporating advanced techniques such as hybrid retrieval, reranking, query expansion, and contextual compression Implement parameter-efficient fine-tuning strategies (LoRA, QLoRA, PEFT) to adapt foundation models to domain-specific use cases while optimizing for inference costs and latency Develop intelligent routing and orchestration systems to manage conversation state across multiple specialized AI agents, ensuring sea

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