Staff Machine Learning Engineer

Role Description

We are seeking a highly motivated and experienced Staff MLE 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 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.
• ML Outcomes:
  • Explore and apply advanced machine learning techniques, including large language models (LLMs), deep learning, and graph neural networks.
  • 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.
  • Design and deliver scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices.
  • 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.
  • 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: Proactively guide business teams across different areas using ML models.
  • Engineering: 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.
  • Design and develop multi-agent GenAI systems using state-of-the-art frameworks.
  • Engineer robust Retrieval Augmented Generation (RAG) pipelines.
  • Implement parameter-efficient fine-tuning strategies.
  • Develop intelligent routing and orchestration systems.
  • Build evaluation frameworks to measure and improve LLM performance.
  • Integrate LLM solutions with existing enterprise architecture.

Qualifications

• PhD in Computer Science, Stats, Economics, or a relevant technical field with at least 5+ years of relevant experience or MS with at least 8+ years of experience in machine learning and software engineering.
• ML Skills: 6+ years of hands-on experience designing, building, and deploying AI (ML, DL, Gen-AI) models.
• GenAI Skills: 4+ years of experience building and deploying AI/ML applications.
• Experience applying agentic AI to design and implement scalable multi-agent systems.
• Strong problem-solving skills with a bias for action.

Requirements

• Experience in the automotive industry, especially in building ML/AI systems while ensuring local and central regulations.
• Experience in model interpretability and responsible AI practices.
• Expertise in data science, advanced experimentation, and visualization techniques.
• Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray).
• Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints.
• Experience with Databricks MLflow for ML lifecycle management and model versioning.
• Hands-on experience with Databricks Model Serving for production ML deployments.

Benefits

• Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/dental/vision, and many nonstandard benefits.