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Job Overview
Location
USA - Remote
Job Type
Full-time
Category
Machine Learning Engineer
Date Posted
March 25, 2026
Full Job Description
đź“‹ Description
- • As a Senior Manager of Machine Learning Engineering at Credit Acceptance Corporation, you will lead the development and deployment of AI-powered solutions that drive strategic value across the company’s flywheel, partnering with business and engineering stakeholders to shape vision and roadmap for innovative ML/AI applications serving dealers, consumers, and team members.
- • You will lead a team of ML engineers in designing, building, and scaling production-grade ML and generative AI systems, including LLMs, deep learning, and graph neural networks, while ensuring security, scalability, and architectural integrity through rigorous code reviews, mentoring, and hands-on problem-solving.
- • Your day-to-day will involve driving execution of ML/AI initiatives by managing priorities and deliverables, translating strategic roadmaps into quarterly plans, and collaborating with cross-functional teams in engineering, product, business operations, and legal to integrate AI solutions into real-world workflows.
- • You will explore and apply advanced techniques such as LLMs, reinforcement learning, recommendation systems, and causal meta-learning to solve complex business challenges, while guiding teams in areas like personalization, growth via data-driven causality, and Gen-AI integration into existing applications.
- • You will foster a culture of engineering excellence and continuous improvement by mentoring team members on design principles, coding standards, and AI productivity tools, while promoting ownership, customer empathy, and one-team collaboration across the organization.
- • Credit Acceptance is an award-winning, Great Place to Work-certified company with a stable financial history and a culture rooted in innovation, professional development, and work-life balance, where your leadership will directly contribute to optimizing success through data-driven decision-making and cutting-edge technology.
- • In this role, you will achieve significant impact by building scalable ML/AI platforms and inference pipelines, improving system reliability and operational efficiency, and leaving a lasting legacy of responsible, high-performing AI systems that empower business teams and enhance customer experiences.
🎯 Requirements
- • PhD in Computer Science, Statistics, Economics, or a relevant technical field with 8+ years of experience, or MS with 10+ years of experience in machine learning and software engineering
- • 8+ years of hands-on experience designing, building, and deploying AI/ML models (including LLMs, transformers, reinforcement learning, recommendation systems, regressions) with strong foundations in mathematics, statistics, and engineering
- • Proven expertise in scaling and maintaining production-grade ML services, including ML/LLM operations (versioning, automation, observability, automated training/monitoring), and ability to balance model complexity with production demands
🏖️ Benefits
- • Excellent benefits package including 401(K) match, adoption assistance, parental leave, tuition reimbursement, and comprehensive medical/dental/vision coverage
- • Eligibility for annual variable bonus of cash and equity (15-30%) based on individual performance
- • Premium compensation potential for candidates residing in major metropolitan areas (San Francisco, Seattle, Boston, New York City, Los Angeles, San Diego)
Skills & Technologies
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About Credit Acceptance Corporation
Credit Acceptance Corporation is a Michigan-based auto finance company founded in 1972. It provides financing programs to automobile dealers that enable them to sell vehicles to consumers, regardless of credit history. The company purchases consumer loans originated by dealers under its Portfolio and Purchase Programs, then services these receivables. Credit Acceptance operates across the United States and reports financial results through its Consumer Loan segment. The company generates revenue primarily through interest and fees on its portfolio of automobile loans.
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