[Remote] Staff Machine Learning Engineer

Note: The job is a remote job and is open to candidates in USA. 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.ResponsibilitiesExplore 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 organizationCollaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plansDrive execution and delivery of ML/AI solutions by managing priorities, deadlines, and deliverables, leveraging your technical expertiseDesign 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 needsTroubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiencyEnsure the security, scalability, and architectural integrity of feature designs through reviews across teamsDeliver hands-on solutions while mentoring other data professionals (including MLEs) within the organizationGuide a team of MLEs across different areas:Mentor team members on design principles, coding standards, and the adoption of AI productivity toolsPersonalize guidance across different surfaces using deep learning methods; personalize layouts with Bayesian contextual multi-armed banditsFoster long-term growth through data-driven causality and incrementalityPower existing applications with Gen AI models and engineering to improve downstream experience and decisionsUsing ML models (such as XGBoost & Causal Meta-Learner-based model, etc), proactively guide business teams across different areasWith engineering partners, build ML and Gen-AI platform and inference pipelines for different types of modelsArchitect and implement enterprise-grade LLM-powered solutions, managing the full lifecycle from business requirements to production deployment, monitoring, and continuous optimizationDesign 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 verificationEngineer robust Retrieval Augmented Generation (RAG) pipelines incorporating advanced techniques such as hybrid retrieval, reranking, query expansion, and contextual compressionImplement parameter-efficient fine-tuning strategies (LoRA, QLoRA, PEFT) to adapt foundation models to domain-specific use cases while optimizing for inference costs and latencyDevelop intelligent routing and orchestration systems to manage conversation state across multiple specialized AI agents, ensuring seamless transitions between different system capabilitiesBuild evaluation frameworks to measure and improve LLM performance across diverse metrics, including factuality, coherence, task completion, and alignment with business objectivesIntegrate LLM solutions with existing enterprise architecture, ensuring compliance with data security policies, authentication mechanisms, and transaction safety requirementsSkillsPhD 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 engineeringML 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 infraGenAI 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 LLMsExperience applying agentic AI to design and implement scalable multi-agent systemsStrong problem-solving skills with bias for actionExperience in the automotive industry, especially in building ML/AI systems while ensuring local and central regulationsExperience in model interpretability and responsible AI practicesExpertise in data science, advanced experimentation and visualization techniquesExperience 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 endpointsExperience with Databricks MLflow for ML lifecycle management and model versioningHands-on experience with Databricks Model Serving for production ML deploymentsDemonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM modelsHands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategiesKnowledge of multimodal AI (text, image, audio integration)Proficiency with GenAI frameworks/tools and technologies such as Apache Airflow, Spark, Flink, Kafka/Kinesis, Snowflake, and DatabricksBenefitsExcellent 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 WorkAnnual variable bonus of cash and equity, between 10 - 20%. Bonus amounts are based on individual performanceCompany OverviewCredit Acceptance is an indirect finance company that helps eligible consumers restart financially. It was founded in 1972, and is headquartered in Southfield, Michigan, USA, with a workforce of 1001-5000 employees. Its website is http://www.creditacceptance.com/.