Machine Learning Engineer, Senior Manager

Credit Acceptance is an award-winning company recognized for its workplace culture and success in the used car finance industry. The company is seeking a highly motivated and experienced Leader of ML and AI Engineering who will lead the development of AI-powered solutions, collaborating with various stakeholders to achieve strategic goals and deliver innovative solutions.

Responsibilities:

• 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

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