Machine Learning Engineer, Senior Manager

Credit Acceptance Corporation•San Francisco, CA

4d•Hybrid

About The Position

Credit Acceptance is hiring a Machine Learning Engineer, Senior Manager to lead the vision and strategic execution of machine learning and AI initiatives, with an emphasis on continuous, long-term value creation. The role collaborates with management and stakeholders to turn strategic roadmaps into quarterly plans, and drives ML/AI solution delivery by managing priorities, deadlines, and deliverables. It combines hands-on technical work with leadership and mentoring of machine learning engineers and other data professionals. The position designs and delivers scalable, secure systems using modern AI/ML technologies and engineering best practices. It also addresses complex technical issues, supports reliability and operational efficiency, and reviews designs across teams to ensure security, scalability, and architectural integrity. The work spans recommendations and personalization, growth measurement, generative AI applications, lifecycle modeling, and ML/Gen-AI platforms and inference pipelines. This is a hybrid position based at a local office in one of the greater Southfield, Michigan; greater Bellevue, Washington; or San Francisco Bay Area hubs. The schedule is two work-from-home days and three in-office days each week. Candidates must live in or be willing to relocate to one of these hubs.

Requirements

PhD in Computer Science, Statistics, Economics, or a relevant technical field with at least 8 years of relevant experience, or a master's degree with at least 10 years of experience in machine learning and software engineering.
At least 8 years of hands-on experience designing, building, and deploying AI models, including machine learning, deep learning, and generative AI models such as reinforcement learning algorithms, recommendation systems, transformers, fine-tuned LLMs, and regression models.
Solid understanding of the mathematics, statistics, and engineering required to build AI/ML infrastructure.
Hands-on expertise scaling and maintaining production-grade ML services, including ML/LLM operations such as versioning, automation, observability, automated training, and monitoring.
Ability to balance ML model complexity with production requirements.
Experience using a test-and-learn approach to identify business opportunities and develop scalable, efficient solutions integrating AI algorithms, ML/LLM operations, and software engineering.
Experience partnering with engineering, product, business operations, legal, and other teams to design, build, and execute solutions.
Strong problem-solving skills and a bias for action.
Must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.

Nice To Haves

Experience in the automotive industry, especially building ML/AI systems while ensuring compliance with local and central regulations.
Experience with model interpretability and responsible AI practices.
Expertise in data science, advanced experimentation, and visualization techniques.
Experience designing and implementing pipelines using DAGs and tools such as Kubeflow, DVC, or Ray.
Ability to build 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 LLMs.
Hands-on knowledge of Chain-of-Thought, Tree-of-Thought, and Graph-of-Thought prompting strategies.

Responsibilities

Lead the vision and strategic execution of initiatives, focusing on continuous and long-term value creation.
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.
Design and deliver scalable, secure systems using current AI/ML technologies and engineering best practices.
Promote high-quality, well-tested systems that address critical product and business needs.
Troubleshoot and resolve complex technical issues to improve reliability, scalability, and operational efficiency.
Review feature designs across teams to ensure security, scalability, and architectural integrity.
Deliver hands-on solutions and mentor data professionals, including machine learning engineers, on design principles, coding standards, and AI productivity tools.
Explore and apply advanced machine learning techniques, including LLMs, deep learning, and graph neural networks.
Guide machine learning engineers working on recommendations and personalization, growth, generative AI, lifecycle modeling, and engineering platforms and inference pipelines.
Use deep learning methods to personalize guidance across surfaces and Bayesian contextual multi-armed bandits to personalize layouts.
Foster long-term growth through data-driven causality and incrementality.
Use generative AI models and engineering to improve downstream experiences and decisions in existing applications.
Use machine learning models, including XGBoost and causal meta-learner-based models, to proactively guide business teams.
Partner with engineering teams to build ML and Gen-AI platforms and inference pipelines for different model types.
Remain compliant with company policies, processes, and legal guidelines; perform other duties as assigned and meet department attendance requirements.