Credit Acceptance
Staff Machine Learning Engineer
Vaga remota de Staff Machine Learning Engineer com fit claro de localização do candidato.
Publicada19 de jun. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Staff Machine Learning Engineer
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
Outcomes and Activities:
- This position will work from home; occasional planned travel to an assigned Southfield, Michigan office location may be required. However, this position is permitted to work at a Southfield, Michigan office location if requested by the team member.
- ML Outcomes: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 organizationGuide 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 banditsGrowth: Foster long-term growth through data-driven causality and incrementalityGen-AI: Power existing applications with Gen AI models and engineering to improve downstream experience and decisionsLifecycle - 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
- 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
- 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: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 banditsGrowth: Foster long-term growth through data-driven causality and incrementalityGen-AI: Power existing applications with Gen AI models and engineering to improve downstream experience and decisionsLifecycle - 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
- 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
- Gen AI Outcomes:Architect 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 requirements
- 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
Details
- 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
- 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: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 banditsGrowth: Foster long-term growth through data-driven causality and incrementalityGen-AI: Power existing applications with Gen AI models and engineering to improve downstream experience and decisionsLifecycle - 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.Demonstrable 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 strategies
- 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
- 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
- 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
- Proficiency with model training/inference frameworks (PyTorch, TensorFlow, Hugging Face Transformers)
- Experience building conversational AI (Text, Voice) , content generation, or code generation systems
- Hands-on experience with building, fine-tuning and deploying multi-modal LLM Models and managing the end-to-end model lifecycle
- Experience partnering with engineering, product, BizOps and other data teams while designing, building and executing solutions
- Deep understanding in at least three of the following areas: data mining, advanced statistics, machine learning, deep learning (incl NLP)
Outcomes and Activities:
- 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.
Required:
- 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
Preferred:
- ML Preference:Experience in the automotive industry, especially in building ML/AI systems while ensuring local and central regulationsExperience 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 endpointsExperience with Databricks MLflow for ML lifecycle management and model versioningHands-on experience with Databricks Model Serving for production ML deployments
- 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
- GenAI Preference: Demonstrable 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 strategiesExperience 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 deploymentsKnowledge 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.Demonstrable 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 strategies
- 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
- 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
- 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.Demonstrable 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 strategies
- 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 and Skills:
- ML Requirements: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 requirementsPassion 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
- 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
- Gen AI RequirementsProficiency with model training/inference frameworks (PyTorch, TensorFlow, Hugging Face Transformers)Experience building conversational AI (Text, Voice) , content generation, or code generation systemsHands-on experience with building, fine-tuning and deploying multi-modal LLM Models and managing the end-to-end model lifecycleExperience partnering with engineering, product, BizOps and other data teams while designing, building and executing solutions Deep understanding in at least three of the following areas: data mining, advanced statistics, machine learning, deep learning (incl NLP)
- Proficiency with model training/inference frameworks (PyTorch, TensorFlow, Hugging Face Transformers)
- Experience building conversational AI (Text, Voice) , content generation, or code generation systems
- Hands-on experience with building, fine-tuning and deploying multi-modal LLM Models and managing the end-to-end model lifecycle
- Experience partnering with engineering, product, BizOps and other data teams while designing, building and executing solutions
- Deep understanding in at least three of the following areas: data mining, advanced statistics, machine learning, deep learning (incl NLP)
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:
- 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
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