Staff Machine Learning Engineer

About the role

The ideal candidate will have a strong technical background in decision science, machine learning, and generative AI with a proven track record in solving business problems and implementing large-scale automated solutions in partnership with the respective engineering teams. This position will work from home; occasional planned travel to an assigned Southfield, Michigan office location may be required.

Responsibilities

• Partner with business and engineering stakeholders to formulate the vision to achieve the company's strategic goals and co-lead the roadmap to deliver innovative solutions for dealers, consumers and team members.
• Lead the development of AI-powered solutions across different business areas, including exploring and applying advanced machine learning techniques, designing and delivering scalable, secure systems, troubleshooting and resolving complex technical issues, and ensuring the security, scalability, and architectural integrity of feature designs.
• Mentor team members on design principles, coding standards, and the adoption of AI productivity tools, and recommend personalized guidance across different surfaces using deep learning methods.
• Guide a team of MLEs across different areas, fostering long-term growth through data-driven causality and incrementality, and power existing applications with Gen AI models and engineering to improve downstream experience and decisions.
• 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.
• 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.
• Build intelligent routing and orchestration systems to manage conversation state across multiple specialized AI agents, ensuring seamless transitions between different system capabilities.
• Develop 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.

Qualifications

• 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.
• 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.
• 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.

Skills

• Customer Empathy
• Engineering Excellence
• One Team
• Owner’s Mindset

Target Compensation

A competitive base salary range from $153,759 – $225,514. This position is eligible for an annual variable bonus of cash and equity, between 10 - 20%. Bonus amounts are based on individual performance. Final compensation within the range is influenced by many factors including role-specific skills, depth and experience level, industry background, relevant education and certifications.