Note: The job is a remote job and is open to candidates in USA. Credit Acceptance is an award-winning company recognized nationally for its workplace culture. They are seeking a highly motivated and experienced Leader of ML and AI Engineering to develop AI-powered solutions, collaborate with stakeholders, and drive the strategic execution of machine learning initiatives.
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:
- Mentor team members on design principles, coding standards, and the adoption of AI productivity tools
- Personalize guidance across different surfaces using deep learning methods; personalize layouts with Bayesian contextual multi-armed bandits
- Foster long-term growth through data-driven causality and incrementality
- Power existing applications with Gen AI models and engineering to improve downstream experience and decisions
- Using ML models (such as XGBoost & Causal Meta-Learner-based model, etc), proactively guide business teams across different areas
- With engineering partners, build ML and Gen-AI platform and inference pipelines for different types of models
Skills
- 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
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
- W2 option
Company Overview
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