Machine Learning Engineer, Senior Manager

Credit Acceptance is a provider of used and new car financing across the country. The Senior Manager, Machine Learning Engineering will lead the vision, strategy, and development of AI-powered solutions across business areas, partnering with business and engineering stakeholders to deliver scalable ML and generative AI systems. The role also guides MLE teams, mentors data professionals, and drives production-grade machine learning platforms and inference pipelines.

Responsibilities:

• 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
• Lead the vision and the 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 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
• Explore and apply advanced machine learning techniques, including large language models (LLMs), deep learning, and graph neural networks, to solve complex challenges across 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
• Remain compliant with our policies processes and legal guidelines
• All other duties as assigned
• Attendance as required by department

Requirements:

• PhD in Computer Science, Stats, Economics, or a relevant technical field with at least 8+ years of relevant experience or MS with at least 10+ years of experience in machine learning and software engineering
• 8+ years of hands-on experience designing, building and deploying AI (ML, DL, Gen-AI) models, including Reinforcement Learning algorithms, Recommendation systems, Transformers, fine-tuned LLMs, Regressions, etc., with a solid understanding of mathematics, statistics, and engineering needed to build such infrastructure
• Hands-on expertise in scaling and maintaining production-grade ML services, with a strong focus on ML/LLM Operations (versioning, automation, observability, automated training and monitoring, etc.) and ability to balance ML model complexity with production requirements
• Passion for identifying new business opportunities and experience of using a test and learn approach to bring scalable and efficient solutions integrating AI algorithms, ML/LLM Ops, and s/w engineering
• Experience partnering with the engineering, product, business operations, legal and other teams while designing, building, and executing solutions
• Strong problem-solving skills with bias for action
• Experience in automative 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
• 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, model quantization, and quantization-aware fine-tuning of LLM models
• Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies