Machine Learning Lead Engineer

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.

Requirements

• Required Skills
• Proficiency in AI development tools (GitHub Copilot, Claude, GPT-4) for ML development with ability to validate AI outputs for production readiness.
• Understanding of agentic frameworks (AWS AgentSquad, AWS Strands, LangChain, agent patterns) with progression from basic configuration to custom enterprise system design.
• Knowledge of AI ethics, responsible AI practices, and governance frameworks for business-critical ML deployment.
• Ability to leverage AI like Co-Pilot for technical communication to stakeholders and cross-functional collaboration.
• Commitment to continuous learning in AI-augmented data science and responsible AI use.
• Required Qualifications
• Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship. No OPT, CPT, STEM/OPT or visa sponsorship now or in f

Benefits

Additional Information

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

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