Machine Learning Engineer, Senior Manager

Description

At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation.

Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact.

Outcomes and Activities:

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

Competencies: The following items detail how you will be successful in this role.

Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer’s shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience.

Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions.

One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively.

Owner’s Mindset: Owner’s Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business.

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
• Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California.
• Preferred
• 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
• Target Compensation: This position offers a competitive base salary, with a range of $221,225 - $309,716.
• This position is eligible for an annual variable incentive of cash and equity, between 15-30% of base salary. Actual amounts are based on individual performance.
• Final compensation within the range is influenced by many factors including assigned hub location, role-specific skills, depth and experience level, industry background, relevant education and certifications.
• This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at the local office.
• INDENGLP
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• LI-Hybrid

Benefits

• Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work
• Our Company Values:
• To be successful in this role, Team Members need to be:
• Positive by maintaining resiliency and focusing on solutions
• Respectful by collaborating and actively listening
• Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self-awareness and making quality decisions
• Direct by effectively communicating and conveying courage
• Earnest by taking accountability, applying feedback and effectively planning and priority setting
• Expectations:
• Remain compliant with our policies processes and legal guidelines
• All other duties as assigned
• Attendance as required by department
• Advice !
• We understand that your career search may look different than others. Our hiring team wants to make sure that this would be a fit not just for us, but for you long term. If you are actively looking or starting to explore new opportunities, send us your application!
• P.S .
• We have great details around our stats, success, history and more. We’re proud of our culture and are happy to share why – let’s talk!
• Required degrees must have been earned at institutions of Higher Education which are accredited by the Council for Higher Education Accreditation or equivalent.
• California Residents: Please click here for the California Consumer Privacy Act (CCPA) notice regarding the personal information Credit Acceptance may collect from you.
• Play the video below to learn more about our Company culture.