Machine Learning Engineer, Senior Manager

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 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 the respective 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 play a pivotal role in the success of this mission as you will 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.
This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at the local office.
Outcomes and Activities:
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
Competencies: The following items detail how you will be successful in this role.
Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer’s shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience.
Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions.
One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively.
Owner’s Mindset: Owner’s Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business.
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
Preferred
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
Target Compensation: A competitive base salary range from $184,354 – $270,386. This position is eligible for an annual variable bonus of cash and equity, between 15-30%. Bonus amounts are based on individual performance. Final compensation within the range is influenced by many factors including role-specific skills, depth and experience level, industry background, relevant education and certifications.
Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California.
INDENGLP
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#LI-Hybrid
Benefits
Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work
Our Company Values:
To be successful in this role, Team Members need to be: