About the role
Company Cox Automotive - USA Job Family Group Data Intelligence & Science Job Profile Machine Learning Lead Engineer Management Level Manager - Non People Leader Flexible Work Option Hybrid - Ability to work remotely part of the week Travel % Yes, 15% of the time Work Shift Day
Compensation
Compensation includes a base salary in the range of $134,900.00 - $224,900.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate’s knowledge, skills, and abilities. Position may be eligible for additional
compensation that may include an incentive program. Job Description We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting-edge research with the responsibility of building a culture of continuous learning and knowledge sharing. You'll lead efforts to identify, evaluate, and prototype emerging ML technologies while establishing our company as a thought leader in the ML community. Applies AI and Machine Learning (AI/ML) principles to design, test, and scale frameworks, systems, and models for big data predictive applications. Develops AI/ML-powered solutions based on business needs. Researches, implements, and tests machine learning methods to create product features, automate workflows, extract insights from data, and improve data quality. Structures, trains, and deploys models to learn from complex data across multiple modalities (e.g., structured, unstructured, image, video, audio) to uncover patterns and develop practical solutions. Possesses deep knowledge in at least one sub-area of machine learning, such as deep learning, generative AI, computer vision, optimization, predictive models, or causal machine learning. WHAT YOU’LL DO Key
Responsibilities Accelerate ML development using AI tools for code generation, feature engineering, optimization, and validation Stay up to date with advancements in ML, AI, and emerging technologies Design, build, and maintain ML models, algorithms, and robust pipelines for data processing, training, and inference Optimize model performance, scalability, and reliability in production environments Collaborate cross-functionally to translate model insights into business value and communicate project updates Contribute to ML infrastructure improvements, best practices, and documentation Partner with engineering teams to integrate AI-enhanced models and establish automated monitoring frameworks. Establish AI governance practices including bias detection, interpretability, compliance monitoring, and responsible deployment. Mentor teams in AI adoption, share best practices, and promote responsible AI innovation culture. Lead AI transformation initiatives including tool evaluation, governance development, and strategic adoption planning. Analyzes complex data sets to solve real-world business and customer use cases. Performs end-to-end development of machine learning models May assist with or lead the development of industry whitepapers or other technical publications. Continuously evaluate AI processes for accuracy, efficiency, and business impact while staying current on emerging technologies. Design agentic workflows for autonomous training, data pipelines, and analytical problem solving appropriate to experience level. Key AI Use Cases AI-Accelerated Model Development: Use GitHub Copilot, Claude Code for rapid ML prototyping, automated feature engineering, and intelligent hyperparameter optimization. Agentic ML Workflows: Understand and deploy (P4+) AWS AgentSquad, AWS Strands, LangChain agents for autonomous training pipelines, multi-step analysis, and collaborative research. AI-Enhanced Model Interpretation: Build on traditional frameworks (SHAP, LIME) with AI tools for enhanced stakeholder communication and automated insights. AI-Powered Research: Leverage manual/autonomous competitive intelligence and research acceleration tools for methodology discovery and algorithm innovation.
