Machine Learning Engineer, Senior Manager

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. The leader will 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.

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:
• 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

Requirements

• 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

Preferred 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)
• Experience with Databricks MLflow for ML lifecycle management and model versioning
• 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
• Experience with Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies