Machine Learning Engineer, Senior Manager

Credit Acceptance Corporation•San Francisco, CA

5d•Hybrid

About The Position

Credit Acceptance is seeking a senior machine learning engineering leader to guide the strategic direction and execution of machine learning and AI initiatives. The role focuses on long-term value creation, translating strategic roadmaps into quarterly plans, and delivering scalable, secure, production-grade solutions that address business and product needs. The position combines hands-on technical work with team leadership, cross-functional partnership, mentoring, and oversight of engineering quality, system reliability, scalability, and architectural integrity. The role guides machine learning engineers working across mentoring, personalized recommendations, business growth, generative AI, lifecycle modeling, and ML/Gen-AI platform engineering. It applies advanced techniques such as deep learning, large language models, graph neural networks, reinforcement learning, and contextual bandits. This position follows a hybrid work model: two work-from-home days and three in-office days each week. Candidates must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California. The position offers a base salary of $221,225–$309,716 and is eligible for an annual cash and equity variable incentive of 15–30% of base salary. Actual compensation depends on factors including hub location, skills, experience, industry background, education, and certifications. Required degrees must be from institutions accredited by the Council for Higher Education Accreditation or an equivalent accreditor.

Requirements

PhD in Computer Science, Statistics, Economics, or a relevant technical field and at least 8 years of relevant experience; or a master's degree and 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.
Experience with reinforcement learning algorithms, recommendation systems, transformers, fine-tuned large language models, and regression models.
Strong understanding of the mathematics, statistics, and engineering required to build AI/ML infrastructure.
Hands-on expertise scaling and maintaining production-grade ML services, with a focus on ML/LLM operations such as versioning, automation, observability, automated training, and monitoring.
Ability to balance 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.
Required degree must be from an institution accredited by the Council for Higher Education Accreditation or an equivalent accreditor.

Nice To Haves

Experience in the automotive industry, particularly 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, 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 generative AI 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 large language models.
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 advanced AI/ML technologies and engineering best practices.
Promote the development of 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 while mentoring data professionals, including machine learning engineers, in design principles, coding standards, and AI productivity tools.
Guide machine learning engineers working across recommendations, business growth, generative AI, lifecycle modeling, and engineering platforms.
Apply deep learning methods to personalize guidance across surfaces and Bayesian contextual multi-armed bandits to personalize layouts.
Support data-driven growth through causal and incrementality analysis.
Use generative AI models and engineering to improve existing applications, downstream experiences, and decisions.
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 generative AI platforms and inference pipelines for different model types.
Explore and apply advanced machine learning techniques, including large language models, deep learning, and graph neural networks, to organizational challenges.
Partner with engineering, product, business operations, legal, and other teams to design, build, and execute solutions.
Remain compliant with company policies, processes, and legal guidelines; perform other assigned duties and meet attendance requirements.