Staff Machine Learning Engineer (Employer of Record)

Credit Acceptance is proud to be an award-winning company with 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 in the United States.

In this role, you will work as a dedicated member of a globally distributed team, partnering closely with business partners in the U.S. to design, build, and scale solutions that directly impact our customers and operations. While your legal employer will be our EoR partner, you will be fully integrated into our Credit Acceptance team for day-to-day work and collaboration.
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:

- 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 acros