Staff Machine Learning Engineer

Credit Acceptance is an award-winning company recognized for its exceptional workplace culture. They are seeking a highly motivated and experienced Staff Machine Learning Engineer to lead the development of AI-powered solutions, collaborating with business and engineering stakeholders to achieve strategic goals and deliver innovative solutions.

Responsibilities:

• Explore and apply advanced machine learning techniques, including not limited to large language models (LLMs), deep learning, and graph neural networks, to solve complex challenges across the organization
• 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
• 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
• Architect and implement enterprise-grade LLM-powered solutions, managing the full lifecycle from business requirements to production deployment, monitoring, and continuous optimization
• Design and develop multi-agent GenAI systems using state-of-the-art frameworks (LangChain, LlamaIndex) to orchestrate complex workflows across retrieval augmentation, data operations, and compliance verification
• Engineer robust Retrieval Augmented Generation (RAG) pipelines incorporating advanced techniques such as hybrid retrieval, reranking, query expansion, and contextual compression
• Implement parameter-efficient fine-tuning strategies (LoRA, QLoRA, PEFT) to adapt foundation models to domain-specific use cases while optimizing for inference costs and latency
• Develop intelligent routing and orchestration systems to manage conversation state across multiple specialized AI agents, ensuring seamless transitions between different system capabilities
• Build evaluation frameworks to measure and improve LLM performance across diverse metrics, including factuality, coherence, task completion, and alignment with business objectives
• Integrate LLM solutions with existing enterprise architecture, ensuring compliance with data security policies, authentication mechanisms, and transaction safety requirements

Requirements:

• PhD in Computer Science, Stats, Economics, or a relevant technical field with at least 5+ years of relevant experience or MS with at least 8+ years of experience in machine learning and software engineering
• ML Skills: 6+ 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, Causal Inference, Regressions, etc., with a solid understanding of mathematics, statistics, and engineering needed to build such infra
• GenAI Skills: 4+ years of experience building and deploying AI/ML applications including Reinforcement algorithms, Recommendation systems, Generative AI etc. with solid understanding of mathematics, Computer Science, foundation concepts and engineering behind building AI applications and LLMs
• Experience applying agentic AI to design and implement scalable multi-agent systems
• Strong problem-solving skills with bias for action
• Experience in the automotive 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
• 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
• Knowledge of multimodal AI (text, image, audio integration)
• Proficiency with GenAI frameworks/tools and technologies such as Apache Airflow, Spark, Flink, Kafka/Kinesis, Snowflake, and Databricks