Company
Cox Automotive - USAJob Family Group
Job Profile
Management Level
Flexible Work Option
Travel %
Work Shift
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
- 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
- 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
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
- 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
- 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 in the ML community through talks or open source work
- Knowledge of new areas such as LLMs, agents, foundation models, multimodal AI, or quantum ML
- Build a culture of testing, learning, and smart risk taking
- Build agreement on research priorities while keeping speed high
- Grow talent through mentoring in both technical skill and research method
- Share complex results and their meaning with people at every level
- Lead by example through curiosity, careful method, and open sharing
- Connect new research to real business use
- Build the team into a known center for ML experiments
Drug Testing
Benefits
About Us
Skills Required
- Authorized to work in the United States without sponsorship (no OPT/CPT/visa sponsorship)
- Bachelor's degree +6 years, Master's +4 years, Ph.D. +1 year, or 14 years experience with no degree
- 6+ years' experience in machine learning-focused work
- Skilled in AI development tools (Claude, GPT) and ability to validate AI output before production
- Understanding and hands-on experience with agent frameworks (AWS AgentSquad, AWS Strands, LangChain)
- Knowledge of AI ethics, responsible AI practices, and governance for business-critical ML
- Hands-on experience sourcing, deploying, and running open-source models in local or cloud environments
- Ability to set up model serving infrastructure and run open-weight models end-to-end (weights, dependencies, inference)
- Comfort fine-tuning, quantizing, or otherwise adapting open-source models to task and hardware constraints
- Track record of leading new projects from idea to proof of concept
- Deep skill in more than one ML area and active knowledge of new research
- Experience building and leading research or innovation teams
- Strong communication skills for both technical and executive audiences
- Proof of sharing knowledge through papers, talks, or similar outputs
- Must live within a commutable distance to Atlanta
- Experience in corporate research labs, innovation teams, or technology consulting
- Background moving research into business use
- Established reputation in ML community via talks or open source work
- Knowledge of LLMs, agents, foundation models, multimodal AI, or quantum ML
Cox Automotive Inc. Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cox Automotive Inc. and has not been reviewed or approved by Cox Automotive Inc..
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Healthcare Strength — Health coverage is described as robust, encompassing medical, dental, vision, and mental‑health resources. Wellness and wellbeing programs further enhance the value of the package.
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Retirement Support — Retirement offerings feature a competitive employer match and well‑supported plan structure that stand out within total rewards. These elements are viewed as adding meaningful long‑term value.
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Parental & Family Support — Paid parental leave and family‑building resources, including adoption and fertility support, are emphasized. Childcare and caregiving supports reinforce a family‑friendly package.
Cox Automotive Inc. Insights
What We Do
Cox Automotive is a global automotive services and technology provider that offers a comprehensive suite of solutions for car shoppers, auto manufacturers, dealers, lenders, and fleets.







