Machine Learning Lead Engineer

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 Cox Automotive is hiring a Machine Learning Engineer Lead for the AI Accelerator team. The role spans three areas. The Lead builds and scales machine learning models across the company, from design through production, and brings deep skill in one area such as deep learning, generative AI, computer vision, optimization, or causal machine learning. The Lead also sets the model selection strategy for the team and owns AI governance policy, including bias checks, compliance tracking, and audit trails. The Lead builds evaluation systems that measure whether AI agents and models perform as expected, using the GenAI Eval Framework. WHAT YOU'LL DO: Key Responsibilities Design, build, and maintain ML models, algorithms, and pipelines for training, inference, and production Use AI tools such as Claude Code to speed up coding, feature work, and testing Build ML infrastructure, monitoring, and documentation with engineering partners Turn model results into business value, and share progress with stakeholders across teams Coach teams on AI adoption, and lead AI transformation work, from tool testing to rollout Track new advances in ML and AI, and publish or present findings through papers and talks Design agent based workflows for training, data pipelines, and analysis, matched to team skill level Model Selection & AI Governance Strategy Own the Model Optimization pillar of the agentic AI framework, and set the model selection strategy for the team Pick between Claude Haiku, Sonnet, and Opus based on task complexity, cost, and speed needs Build clear rules for when to use each model, and document the reasoning for each choice Track model cost across projects, and report spend to leadership Own AI governance policy, including bias checks, compliance tracking, and audit trails Work with legal and compliance teams to meet AI regulations Report on AI risk and model use across the AI Accelerator portfolio Evaluation Systems (Agentic AI Framework) Build and run evaluation systems for AI agents using the GenAI Eval Framework (GEF) Check answer correctness, task completion, tool selection, and context quality, not just the final output Calibrate LLM as judge scores against human baselines through agreement analysis Curate golden datasets with human labels and short critiques for each agent type Compare evaluation runs before and after a prompt or model change, and flag quality shifts Build synthetic conversations to test agents before they reach production Watch for drift, and trace the root cause when agent quality moves away from baseline Set standards for how teams test and approve new models before rollout, and own the hardest evaluation problems as the field matures WHO YOU ARE: Required Skills Skilled in AI development tools (Claude, GPT- for ML work, with the skill to check AI output before production use Understanding of agent frameworks (AWS AgentSquad, AWS Strands, LangChain, agent patterns), from basic setup to custom enterprise design Knowledge of AI ethics, responsible AI practice, and governance rules for business critical ML work A steady habit of learning in AI augmented data science and responsible AI use Skill in comparing AI models on cost, speed, and output quality, and matching each model to the task Hands-on experience sourcing, deploying, and running open-source models in local or cloud environments Ability to set up model serving infrastructure and get open-weight models running end to end (weights, dependencies, inference) Skill in benchmarking open-source models against hosted/proprietary options on quality, cost, and latency to inform build-vs-buy decisions Comfort fine-tuning, quantizing, or otherwise adapting open-source models to task and hardware constraints 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 future. Bachelor's degree in a related field and 6 years of experience, or a master's degree and 4 years, or a Ph.D. and 1 year, or 14 years of experience with no degree 6+ years' experience working in Machine Learning focused work Skilled in analytical thinking, consulting, requirements work, system and technology integration, and comfort with new technology Skilled in working with intent, clear communication, building trust, driving new ideas, and pushing for high quality work Other duties as needed A track record of leading new projects from idea to proof of concept Deep skill in more than one ML area and knowledge of new research Strong background in testing technology, studying competitors, and planning strategy Proof of sharing knowledge through papers, talks, or similar work Experience building and leading strong research or innovation teams Strong communication skills for both technical and executive audiences A strong network in the ML research community Experience with research partnerships and joint work Must live within a commutable distance to Atlanta Preferred Qualifications Experience in corporate research labs, innovation teams, or technology consulting A record of finding and rolling out breakthrough technology Background moving research into business use A strong name i