Director, Machine Learning Engineering

Credit Acceptance is an award-winning company recognized for its world-class culture and commitment to professional development. The AIML Director will define and drive the enterprise strategy across AI, Machine Learning, and Generative AI, leading a high-performing organization to ensure scalable and innovative solutions that deliver measurable business impact.

Responsibilities:

• Define and communicate the long-term vision for ML/AI applications and engineering, aligning with corporate strategy
• Partner with senior executives, product, and engineering leaders to prioritize initiatives and allocate resources effectively
• Oversee design, development and deployment of enterprise-scale ML/AI solutions, inference pipelines, and respective technical roadmaps
• Ensure operational excellence in ML/LLM Ops, including automation, observability, and lifecycle management in partnership with the AIML platform engineering team
• Champion adoption of cutting-edge techniques (Deep learning, recommendation engine architecture, graph neural networks, causal inference, LLMs, RLs, etc)
• Drive responsible AI practices, model interpretability, and compliance with regulatory requirements
• Build and mentor a high-performing organization of managers and senior engineers
• Foster a culture of continuous learning, experimentation, and engineering craftsmanship
• Establish standards for security, scalability, and architectural integrity across ML/AI systems
• Implement robust governance for data privacy, ethical AI, and risk mitigation

Requirements:

• PhD in Computer Science, Statistics and related field with 10+ years in Machine Learning engineering (preferred); or MS with 12+ years of experience
• Minimum 5 years in senior leadership roles managing managers and large engineering teams
• Proven track record in building production-grade ML systems with strong problem-solving skills
• Hands-on experience building different types of models (ex: large-scale real-time Recommendation models, Causal Inference, Offer optimization, RL, multi-layer DL algorithms, Propensity, Churn, etc.) and scientific solutions
• Deep knowledge of ML lifecycle tools (MLflow, Kubeflow), distributed systems, and cloud-native architectures
• Passion to solve problems and drive value-based transformative changes
• Ability to influence C-suite stakeholders and communicate complex technical concepts clearly
• Experience in strategic planning, budgeting, and organizational scaling
• Experience building, training and developing engineering teams